Nicolas Lainé

Voice, sound, melody: Music as a training tool and a way to interact with elephants in Northeast India

The training of the animal is a crucial period within human-elephant interspecies community. It is the time when man and animal get to know each other, define modalities and forms of communication, and create a common intelligible world which will be used in their future interactions. This initial contact is also crucial in the development of attachment and bonding: their long term partnerships will evolve on the basis of this primary encounter.

In Northeast India the Khamtis, as well as other local populations, address chants to newly captured pachyderms during each step of their integration into the human world. What are the peculiarities of these chants and how can we define them? What roles do they play in the training process? Within the interspecies communication, what are the differences between voices (commands), sounds and melodies?

This chapter purposes to explore the various functions (pedagogical, communicative and social) of chants during elephant training, by studying their contents (their lyrics, forms and rhythms). The analysis will support the argument that, primarily used as an intangible tool for training elephants, music is constitutive of the interspecies community. Sung on various occasions after training, chants are also part of the vocal communication used by the Khamtis to interact with animals in their long partnership.

Introduction -Music, Training and Elephants

At the beginning of 19th century an experiment was made at the Ménagerie du Jardin des Plantes in Paris. A musical band was sent to play on the upper part of the stall in which two elephants, Hanz and Parkies, were kept. A perfect silence was initially procured, some of the elephant's favorite foods were given to the animals to engage their attention, and then the musicians started playing. The music no sooner struck the ears of elephants than they ceased from eating, and turned in surprise to observe from where the sound was produced. If the view of musicians and spectators initially brought fear to them, the music soon overpowered their fears, and all of their emotions became completely absorbed by the music till the end of the concert [START_REF] Houel | Histoire naturelle des deux éléphants, mâle et femelle, du Muséum de Paris, venus de Hollande en France en l'an VI[END_REF]). This experiment was described as "curious", and was simply taken as an anecdotic story by American behaviourists in the last century. Nevertheless it appears that for some local populations daily engaged with elephants, the sensitivity of elephants to music is something they are using in their dayto-day relationships, and more specifically at the time of elephant training.

The initial training of an elephant is indeed a crucial period within the human-elephant interspecies community. It is the time when humans and animals get to know each other, define modalities and forms of communication, and create a common intelligible world which will be used in their future interactions. This initial contact is also crucial in the development of attachment and bonding: their long-term partnerships will evolve on the basis of this primary encounter. Training elephants gives the opportunity to everyone (human and animal) to learn from each other and to reveal respective temperaments and characters. Once this period is completed, the elephant starts his new life among humans and other congeners. He will then continue to learn and no doubt to develop specific capacities, commands and attitudes according to the work he is likely to perform in his/her career (tourist's safari, timber hauling, patrolling, etc.). This chapter deals with the role of music both used as a training tool and a way to interact with elephant among the Tai-Khamtis in Northeast India. The Khamtis are comprised of ten thousand individuals only. Linguistically related to the Shan, mostly found in Burma but also in China and Thailand are Buddhists and have their own alphabet. In the middle of the 18th century, they are detached from Khamti groups found scattered especially in the current Kachin state of Burma. Since their migration to North-East India, they live and work with elephants.

Among the Khamtis the training of elephant occurred few days after his capture. [1] According to Khamti's view, at the term of this process, the forest elephant (chang thun) becomes a village one (chang man). Such transformation, which imposed to the animal is irremediable. The young elephant will then start a new life among human and other village elephants. This process goes by two step: the elephant should first be habituated with the human presence at his sides, then learning commands and basics of his new living conditions. During the two steps of this process, two specifics chants are continuously intoned by the trainer to the newly caught elephant. They are constantly accompanied by tapping and caresses on the animal. Considered as mandatory by all the phandis (elephant catchers) and mahouts interviewed during fieldwork, I was told that through the sounds of melody, words exert power over the animal. And according to Khamtis experts, it is only by singing that the elephant will listen to them. [3] Throughout my fieldwork, I also used to observe Khamtis singing chants to their elephants on various occasions in their day-to-day activities. As for example while bathing the animal, or at work, in forestry activities it has happened to me to observe a mahout singing to his elephant if the animal, for some reasons, was unmotivated to finish his daily work.

In Northeast India, the used of music to train elephants is a shared practice between the Khamtis and other local populations [4] considered as specialists of elephant domestication. In Assamese language -the lingua franca for the entire northeast -there is a particular expression to qualify such chants: hati xikuwa geet, which literally means "song to train elephants" (Moran, 2007 : 256). More generally, singing to elephants during their training is a well-known and observed practice throughout Asia and Southeast Asia since time out of mind. In his journal "Indica", Megasthene, Ambassador of Seleucus during the Chandragupta Maurya reign mentions that Hindu people used to chant to elephants to calm them down and used a string instrument to that effect. Recalling a situation in which a calf elephant was refusing to be fed by its mahouts, he mentioned: "But it is still angry with them, and takes no notice of them. To what device do they then resort? They sing to it their native melodies, and soothe it with the music of an instrument in common use which has four strings" (McCrindle, 1877: 93). More recently such practices were mentioned by several observers or elephant specialists at the time of elephant training. This is the case in India (Barua 1997;[START_REF] Bertrand | À la recherche des éléphants sauvages[END_REF]Stracey 1963), in Laos [START_REF] Fiasson | Au Laos avec mes hommes et mes éléphants[END_REF], in Myanmar (U Toke [START_REF] Gale | Burmese Timber Elephant[END_REF] and in Nepal [START_REF] Locke | The ethnography of captive elephant management in Nepal: a synopsis[END_REF]). In the Kingdom of Siam (Thailand), F.H. [START_REF] Giles | An Account the Rite and Ceremonies Observed at Elephant Driving Operations in the Seaboard Province of Lang Suan, Southern Siam[END_REF] reports that songs were especially composed for the white elephants, considered as the noblest of animals. Such songs were sung to drowse or calm the pachyderms. Still in Thailand, Raymond Plion-Bernier mentions that during the training of newly caught elephant, Brahmins, who assisted at the ceremony, chanting their religious manuscripts accompanying the expert hunter in his operation [START_REF] Plion-Bernier | Fêtes et cérémonies de Thailande[END_REF].

While music appears present across Asia very little information has been given so far on the content of these songs, on their form and their roles especially during the training process. Based on my observations I will describe the context (implementation of speech, moments of speech) in which chants are used during elephant training and make a content analysis (speech, form, rhythm) in each of the two training steps. I propose here to explore various functions of this particular form of singing by answering the following questions: What are the peculiarities of these chants and how can we qualify them? What roles do they play in the training process? Within the interspecies communication, what are the differences between voice commands, sounds and melodies?

The habituation of elephant to the human presence Among the Khamtis, the first step of elephant training starts about ten days after a newly caught animal is bring back to the village. Ten days is the time necessary for the Khamtis to find a place in which the operations will take place, and to prepare the requisite materials (ropes, etc.). This is a camp located at the border of the village and if possible close to a stream, so as to bathe the animal.

Before the training starts, the trainer has to make ropes and build the khemari (in Assamese) or lak chang (in Tai) which is the place where the first step will be held. It is a wooden structure made of two logs sufficiently spaced to lodge the animal. [5] The logs are driven into the ground obliquely so as to increase their resistance.

During this period, it is customary to leave the young pachyderm alone in the forest, securely tied to a tree. No food is given to him in order to weaken the pachyderm; [6] the mahout only comes to visit him twice a day to ensure that there is no danger and to check the ropes and nodes.

Once the lak chang is built the first operation consists in lodging the animal at the centre of it. The elephant is tied in such way that he should not be able to move freely. His hind legs are tied together and pulled back, while his forelegs are directed forward to the unbalance and prevent him from using his strength. During his first immobilisation his ear is pierced with a long heated iron rod, this hole will serve during the second step of the training.

The first step aims at making the animal accept the human presence at his side. It is of making accept the elephant all that is involved in human presence at his side. The first voice, the touch, but also visual contact and smell. This primary socialisation is mandatory before teaching him commands. The sequences consist in petting, massaging and rubbing every part of the elephant's body while reciting a chant to the animal. During each session, in unison, all the people kept on singing, while strongly touching the animal. The chant is enacted at the very moment when tactile contact with elephant starts. The animal is generally trumpeting loud and trying to break free from the ropes, but as the sessions go in morning and evening, he gradually accept to be touched, and trainer starts loosening some of the ropes.

At night, the animal is also tied to the lak chang. During this period, trainer and assistants, go flaming torches around his body to get him used to the heat and make him not to fear fire. [7] Moreover, according to a Khamti belief, forest spirits (phi thun) live on the skin of the animal and cling to the hair (khon chang) thereof. Thus, by passing of torches, the Khamti-trainer intend to burn hairs and then make the spirits return to the jungle. By doing so men sing the same initial chant to the animal [8].

After successive sequences occurred in the morning, afternoon and during night time, it is when the trainer can sit on the back of the animal, without any aggressive or negative attitudes of his side, that this first step is considered as completed. It is then time for the juvenile elephant to continue his apprenticeship by learning some commands. This first step is no doubt the most difficult and painful for the elephant (native and foreign experts often call this period "breaking-in-phase", or "breaking the spirit of the elephant". In Kerala (South India), this phase is called "kettiazhikkal" [9] [START_REF] Panicker | Handling of elephants[END_REF]. When I questioned them, it was difficult to my interlocutors to justify coercive methods used in this first step, but also what precedes it: the fact of starving animals. They replied me that it should be as short as possible. It generally lasts for 7 to 10 days. Actually, the duration depends not only on the nature and size of the elephant, but also the experience of the trainer. On that point, it must be said that Khamtis recognise a good elephant trainer's skills if he manages to complete this step in less than ten days. Within this short period, he has to gain the trust of the animal and to forge links and bonds with him as for their future interactions.

At last, it must be noted that this first step is hard for men too, which should ensure and constantly relay around the animal so as not to let him break. Some Khamtis have told me that they refuse to sleep during this time because the elephant is likely to regain strength and become stronger, which would extend this step further. In some cases, it can have dramatic outcomes: some elephants die because they refuse to eat. In fact, a sign that the familiarisation goes well is when the animal is willing to be fed by the human 's hand.

We will now focus specifically on the study of the chant sung throughout this stage. An analysis of the content of this chant gives indications regarding the nature of the relationship that the Khamtis seek to establish with the elephant and their intention towards the animal.

The transcription shows that the Khamti-trainer addresses the elephant using the interpersonal pronoun "toy". In Assamese, close to Bengali language "toy" is the interpersonal pronoun employed between people of the same age, or with relatives, it is also the pronoun used when addressing to a child. To lower caste or to someone one berates that. (This interpersonal mode of communication is opposed to "apuni" used with elder or respectful person, and / or with "tumi").

In the chant, when the trainer wants to call the elephant, he uses the affectionate term "babu", generally uses to call infants (alternatively if the trained elephant is a female they use "maine", the feminine equivalent of "babu"). Actually, at the time of his training, the elephant does not have a name; it is only after this process that the owner will assign him a name.

These two points, the use of familiarity "toy" and the term "babu", show that the trainer seeks to establish an intimate relation with the elephant. Nevertheless, the trainer sings in an authoritative mode, as demonstrated by the use of the imperative present which expresses an order or advice. By these injunctions, the elephant is encouraged to be attentive and to trust him the trainer ("Stretch out your ears and listen, open your eyes and see"). This last point is also emphasised by the form and verb tenses in the narrative and the story given to the animal. Verbs are in the mode of the present imperative, the modal used to express orders or advice. Here, the elephant is invited to be attentive to what he is told ("Hold out your ears and listen; open your eyes and look") and to trust the human. The man remains firm; he is given injunctions but seeks to make the animal attentive, in the same way as in a waking song for children.

If we look at the register now, the transcription reveals a series of contrasts between the life of wild elephants and the living condition of elephants among men. Lyrics refer to the thematic of jungle ("habi") in opposition to that of men ("Manu"). As for example, the mountain ("pahar") is opposed to the plain ("boyam"). Moreover, the trainer asks the elephant to accept food from his hand by saying: "In the mountains, you ate bamboo shoots; in the plain, you take the food of your grabber."

Interestingly, the trainer gives the elephant a story of his capture, he is also explaining what he is experiencing, "I caught you in the jungle and I tied you to khemari", what will happen to him and how to behave ("take the words of the mahout, understand the words of the mahout"). Lyrics are therefore addressed directly to the animal, requiring him to accept human presence at his side ("Leave your heart of the mountain; take the one of the plain"). The trainer asks the elephant to renounce to his jungle life and to live within human society. We should remember that Khamtis are plains people, while elephantsspecially during the monsoon season, used to go in the mountains.

This study on the meaning of the lyrics reveals an educative register close as a parent do with children. It is based on affection and authority.

So far I have discussed the content and form of the chant; I will now look at the style. Verses are short, composed of two or three lines. Most of the verses are concluded by a strong accentuation of the last syllabi or with onomatopoeia sounds ("hoy, hoy"), which Remi Dor (2002) called huchement. In their daily life, and after the training completed, such sounds are usually pronounced by the mahout: they are part of communication used by humans to interact with elephants. At this stage the elephant is not trained yet: we can assert that the use of onomatopoeia here aims at preparing the animal to the vocal communication he will be taught in the second step of training.

Same couplets are continuously repeated with small rhythmic and lyrics variations. The melody is soft. The trainer sings in a monotone tune. This chant looks like a lullaby for elephants, even though the objective here is not to asleep the elephant since the trainer except a cooperation from him. French ethnomusicologist Michel Imberty reminds that songs can mark duration and regularity, and in the case of lullabies, they help children to structure time (Imberty 2002: 512). The chant here is at midway between speech and song. To some extends, it takes the characteristic of "baby talk" language as highlighted by the American psychologist Daniel N. Stern. The chant is based on repetition and variations sing in a soft melody. According to Stern, for a child this dual principle of is essential in its construction of the self: the musicality of "baby talk" language helps the child to create bookmarks, also to be attentive (Imberty 2002: 513). This is, he adds, a universal and fundamental aspect of psyche for any individual (whatever it is a child or in the present case an elephant). Thus, we can consider that when an elephant is tied to the lak chang, the melody helps him to accept and to build his new living condition: through the hearing of music the wild animal becomes a village one. [START_REF] Imberty | Le bébé et le musical[END_REF] observes that "baby-talk" language has two main functions: to raise the attention of the infant and to calm him when he is afraid (Imberty 2002: 516). These two functions seem to be present in this chant, where lyrics are always accompanied by affective gestures to calm the elephant's fear and / or to keep him aware of what happen to him. Thus the role of chant here is less an educative than a socializing one: just like any parent does with a newborn child, it helps the animal to become familiar with the human environment.

Moreover, Michel Imberty recalls that in the early months of the children socialisation (between three and six months), "what is probably the less important, is what the mother said to his child, the most important thing is the musicality of the sounds she produces" (Imberty 2002: 514). And this seems particularly true if we remind that Khamti sing in Assamese, and that many of them confess me that they don't understand the meaning of the lyrics.

Even if it looks familiar with the "baby talk" language, the aim of the trainer here is not to substitute himself with a mother for the elephant (even though he is aware that domestication implies to respond to some of the animals needs). Actually, during each sequence, the objective is to bring the elephant to replace a behavior by another: more precisely to replace the escape behavior by indifference to human and fire. Thus the objective is not here to produce an "imprinting" as highlighted by the ethologist K. [START_REF] Lorenz | Vergleichende Bewegungsstudien an Anatiden[END_REF]. Furthermore, the phenomenon of imprinting is related to a phenomenon of a spontaneous attraction of the animal, where there is no substantial physical constraint. This is far from the case here, where the animal learns to live within humans in the village, and importantly, the goal is not that it is dependent on a single individual to whom it will simply learn to respond. This first step is more like an habituation process. It aims to establish a co-presence and therefore a lasting bond between the elephant and human.

During this first step, considered as the most painful and difficult, humans continually recite the same chant, the same lyrics to the animal, which settle with the violence of their gestures, often punitive: the elephant is pricked on the trunk so that he cannot use it to charge; when necessary ropes are tightened, to control and prevent the pachyderm to try to get rid of the wooden structure) so that he does not bristled, and no longer seeks to charge the men or to undo the strings. The music here somehow brings a certain form of affection. In some way then The music here would soften the treatment inflicted on the animal. One might even suggest that music also serves to men, it helps them to keep patience and to calm themselves. The Khamti trainers are often tired and exhausted, and although in another register the animal, this period is also grueling for them.

Becoming a village elephant: learning commands

Accepting the presence at his side is only the step first of the integration of a newly caught elephant in the village. The elephant will then continue his socialisation by learning commands through an apprenticeship which constitutes the second step. Before untying the elephant for the lak chang, a rope is wrapped around his neck, the sai kho [10]. This rope has an important role, every village elephant possesses one. This is an example from this that will connect the necessary cables with different activities; it is also against this rope that the mahout will drag his feet in order to transmit gestural commands to the animal when seated on his neck. [11] This second step clearly marks the beginning of the animal's life among men and other congeners. It is longer than the previous one and does not take place in a confined space (the lak chang) at the boundary of the village, but along a field. The purpose of this second step is to teach commands to elephants. It is to advance the elephant and to teach him to respond to simple commands such as agaat ("go ahead"), pischu ("stop"), dhaat ("stop"), soi gum ("turn"), boit ("sit"), at the rhythm of a new chant. To constrain the animal to comply, he is first surrounded by two village adult elephants, to which ropes bind him. The presence of konkie is doubly important in this apprenticeship: on the one hand, without them men would be unable to control the animal since it must be free to move; secondly, because konkie serve as an example to follow. Thus, during the sequences, the congeners are used both as a means of control and security that animal as a way of learning by imitation. During the sessions, two humans are always necessary whatever be the number of village elephant. A trainer sits on the animal back, a second man held the elephant by the sai kho, attached to the original of his ear hole like a leash. The trainer on the elephant back guides the operation. With his feet, he adds the action to the word, taught according to the taught command; he supports with his feet behind the ear. Once arrived at the end of the field; a treat is systematically given to the animal as a reward (a piece of sugar or salt rice ball wrapped in a banana branch). Gradually, as the young elephant incorporates the commands, one konkie will be necessary for continuing his apprenticeship, he will finally end only. Indeed, the second step is considered as completed when the elephant responds to the commands. This second step refers to a more classical associative learning with positive and negative reinforcement. Whenever the animal has responded positively to the command, the trainer congratulates and gives him a leg as food. Moreover, one can also consider that an imitation process is occurring during this step. It is by imitating other village elephants that the apprentice answer positively to commands address to him. It seems that the apprentice imitates his fellow village elephant because he trusts him. In fact, at this step, the elephant has probably not yet significant knowledge to understand what is expected from him. Nevertheless, he may be reassured to see another elephant accomplish a task that he will, in turn, have learned to achieve. In other words, he has confidence in the trust that his fellow village elephant bears on humans. Contrary to the first chant, whose language and lyrics are primarily used to accustom the animal with the human presence, the lyrics sing in the second chant refers to a performative language. Lyrics are a shorters, verses based on the enunciation of the basic commands (such as agaat, pischu, soigum, dhaat) arranged in couplets. Whenever a command is addressed to the animal within the chant, the trainer invites the elephant to perform the mentioned actions ("move forward", "stop", "turn arround", etc.).

Again, contrary to the first chant, the enunciated appears less descriptive than pragmatic. In this case, the lyrics refer to what French linguistic François [START_REF] Recanati | Les énoncés performatifs[END_REF] defines as a declarative speech, the words (i.e. the commands) are less pronounced to say something than to act and to do something. Here more precisely to make to do something. Lyrics here act effectively and efficiency on the animal behaviour: when the elephant moves forward, the corresponding command is mentioned in the verses ("agaat jah"), when the trainer asks the animal to make a turn he includes the associated command ("turn") in the chant; again, before starting over from a point to another, the trainer invites the animal to mark a short pause saying "pischu" (stop).

At first one can consider that words efficiently act on the animal. But in fact the situation seems subtler since this is obviously rather teaching the meaning of words to the elephant than to act according to the addressed command. For example, it is said to the elephant to "go ahead" when he is already moving forward, and so on for the other verbal commands. As for the first chant, verbal commands are addressed on injunctive form in the imperative present tense.

The rhythm of the melody is soft. Nevertheless, when the trainer pronounces commands, he modulates his voice and uses a longer and stronger intonation: at this time, he is no more singing but talking to the animal. For example, the verse that invites the elephant to move forward must be repeated several times before reaching the learner the appropriate conduct. The rhythm of the melody drives the elephant, and the verbal command "agaat" is addressed with emphasis. In this chant, each verse always ends also by a particular command followed by sounds (onomatopoeia).

Thus, this second chant falls into a "spoken-song" (from original German language Sprachgesang), at the intermediate between the act of speaking (the interjections of commands orders) and singing (the melody and lyrics). The tone is stronger, and the rhythm is getting longer when the trainer pronounces a command ("lahé lahé agaat jah!"), while the melody remains soft once the animal performed the said command. There is a strong difference of the voice and sound when the mahout is singing, and when he is asking the elephant to do something. Within this second chant, if the melody allows keeping the elephant attentive, it is however, by use of voice (speaking strongly and imposingly), that trainer transmits vocal commands to animal. Moreover, during this step, some extracts of the first song ("hoy hoy, you know, you know hoy hoy") are inserted at the heart of the second. The trainer re-uses words already known by elephants as to revive and capture the animal's attention. Like in the first song, this second chant serves to mark time for the animal. By introducing words and lyrics already known by the animal the trainer invites the elephant to progress in his socialisation, to mark a "before and an after". Later on, this chant is still employed by mahout in their day-to-day activity with their pachyderms to remind them their condition of village elephants.

In the final phase, when the animal is alone, more than gestural contact or voice, these two channels of interaction (tactile and vocal), which are simultaneous solicitation to achieve the desired action. The verbal command is always accompanied, supported, by a gesture on the corresponding body part of the elephant. Thus, if during the first step the melody has had a socialisation role -in particular the habituation of the animal to the human presence -in the second step, the chant is part of the apprenticeship process in which the elephant learns commands.

Conclusion: Music as a support of attention and interaction between humans and elephants

Music produced in the form of chants sung to elephants accompanies both humans and elephants throughout the two steps of socialisation. These chants are initially used to enter into interaction with the animal. Whenever a training session starts, the human puts his hand on the elephant's body while he begins to recite the chant.

In the first chant, we shall retain especially the importance of musical sounds (the same intensity, the same pace constantly repeated), the register and specific onomatopoeia address to elephants ("hoy, hoy"). This way, human intend to implement a unique way of communicating with the animals still wild and to make it docile. Gestures (tapping and vigorous petting) supplied with the song are made here to reassure the animal, to express tenderness and affection. For cons, the use of stick employed to hit the animal, including his trunk, serves as a coercive means.

In the second chant, instead, the lyrics more closely resemble training techniques, in the sense that it is the combination of the words (orders and injunctions) and gestures that accompany them which allows these techniques to be efficient and the human trainer to achieve the desired result. Moreover, we have noted in this second chant some excerpts from the first one. Throughout their partnership is also occurs that the Khamti recite this chant again in their routine with elephants. When the mahout gives his elephant bath, for example, or during the implementation of common tasks, I happened to see that if the animal does not want to cooperate or seems unmotivated to the task, the mahout hums this chant again as to capture his attention. As already mention, the apprenticeship includes the assimilation of time and appeal to the memory of the individual. It seems here to be the role of chants in elephant's socialisation. Through the practice of reciting chants to elephants, voice can be considered both as a tool and as an integral part of this process. Chants thus carry an educational function for the animals.

The interaction is organized around the chants, which become the support of attention between the Khamtis and elephants. Chants are clearly distinguished from orders and commands pronounced within them. Now if we consider voice as an intangible ideal tool for communicating with animals -as is the case in "briolage" songs for the conduct of animals in Ireland (Griffin-Kremmer, 2001) or in Slovenia [START_REF] Smerdel | Le son des mots, la voix des sons. Sur la culture de communication avec les boeufs de travail en Slovénie", Ethnozootechnie. n° 84[END_REF]-then, the intonation is getting essential. As regards order and obedience, it is first by voice, high and imposing, that human seeks to be understood by pachyderms. The lability of the human voice allows to change the modes of expression of each command to make the elephant understand the requested task (correctives means will be use if it fails). Thus, the voice (commands) clearly distinguishes from the song (lyrics). Moreover, paralinguistic elements (sounds, onomatopoeia) are all the more important to supplements voice and lyrics to be understood by the animal.

Once the two steps the transformation of a forest elephant into a village one is completed, the animal will share his life with other village elephants and the mahout assigned to him. He will then become a part of the village community, and be protected by the houselold spirit of his owner. At that time, he become akin to human in their interiority (Lainé, forthcoming). No special task will be requested to the elephant, until he reached his adult size (around 10 years). The shared life between a village elephant and his mahout is then a long mutual socialisation. The mahout will be in charge of caring, assisting, and feeding him. Their daily activities, especially bath time, are opportunities to strengthen their bonds.

NOTES

[This is an updated version of the paper presented at the Bangalore conference] 1. The data presented in this chapter was collected in Northeast India between 2008 and 2010, as part of my PhD [START_REF] Lainé | Vivre et travailler avec les elephants. Une option durable pour la protection et la conservation de l'espèce. Enquête sur les relations entre les Khamti et les éléphants dans le Nord-Est indien[END_REF] 2. The capture of elephant is a seasonal activity held during the dry season (from October to April) Capture operations involve juvenile elephants, ideally aged from 4 to 5 years old. At that time, male elephants leave the herd to start a solitary life in the forest.

3. Interestingly, the elephant's sensibility to music is found during another important period of Khamti-elephant relationships: at the time of capturing wild elephants. Indeed, when they used to go for catching wild elephants, among the various rituals performed, the catcher always carries a small ivory statue representing a man sitting on the back of an elephant and playing traditional violin. In Khamti cosmology, this man is known as Utingna, (part of the pre-Buddhist pantheon, Utingna the son of King of Kosambi, the ancient name for present Bihar when Gautoma birthplace). The catcher believes that the music played by Utingna has the power to hypnotise and to attract wild elephants, thus, by bringing this statuette with them, wild animals could then be easily caught.

4. Several local populations are considered as experts in elephant in Northeast India. It is the case for the Morans and Singphos population in Upper Assam, and the Rabbah in Lower Assam. The latter even use a stringed instrument (called a dotora) for playing music to elephants. 5. This basically simple type of structure, made of two logs in which the juvenile animal will be placed, is similar to those found in other Southeast Asian countries, in Myanmar [START_REF] Williams | La vie de la nature[END_REF][START_REF] Oo | The Training Methods Used in Myanma Timber Entreprise[END_REF], in Thailand [START_REF] Emourgeon | Les Usages et représentations de l'éléphant en Thaïlande. L'Animal, le Patrimoine, le Symbole[END_REF][START_REF] Giles | An Account the Rite and Ceremonies Observed at Elephant Driving Operations in the Seaboard Province of Lang Suan, Southern Siam[END_REF] and in Laos [START_REF] Fiasson | Au Laos avec mes hommes et mes éléphants[END_REF]. 6. During fieldwork similar practices have been observed among the Morans community; this point is also mentioned in P.D. Stracey (1967).

7. Forest elephants run from fire. This well-known fact is used to prevent wild elephant incursions into fields.

8. Such beliefs are also observed among the Tai-lue in Laos. They consider that a newly caught elephant is inhabited by bad spirits (phi phai. To drive them out, the Tai Lue put a chicken on the elephant's back at the beginning of training. They believe the spirits leave when the chicken leaves the elephant's back. This takes place at the place of training before it begins (Laos 2015, personal field notes) 9. In Malayalam "azhikkal" means "to destroy". 10. In Tai, "sai" means "rope"; "kho" is the neck. 11. For a more detailed account of elephant ropes and accessories, see Lainé 2016. E-mail: nlaine@unistra.fr

Figure 1 :

 1 Figure 1: A juvenile elephant mounted for the first time. Photo: Nicolas Lainé

Figure 2 :

 2 Figure 2:A training session with a newly caught elephant. Photo: Nicolas Lainé

Table 1 :

 1 Lyrics and transcription of the first chant

	Oh dhaat!	Stop!
	Habi mon erebi, manu mon dhoreb	Leave your jungle heart and adopt man heart
	Manu kotha manebi, manu khota	Learn the words from man, listen to them
	unebi	Babu yes, petting
	Babu ye sobye	Leave your wild heart and become a konkie
	Kesa mon erebi, kumkie mon	Accept mahouts words, accept him as your
	dhorebi	owner
	Mahout khota manebi, malik bule	Babu yes, petting
	manebi,	Babu yes, petting
	Sobye babu sobye	Leave your forest heart and adopt man heart
	Sobye babu sobye,	Learn the words from mahout, understand and
	Habi mon erebi, manu mon	Learn them
	dhorebi	Give your ears, open your eyes
	Manu kotha unebi, mahout khota	Whatever be the words and work, they are
	bujebi, mahout khota manebi	good
	Kan meli hunebi, suku meli sabi	Babu yes, petting
	Yeh naam hoy hoy , Yeh kam hoy	Babu yes, petting
	hoy	Yes you know, yes you understand,
	Sobye babu yeh sobye	Yes you know, yes you understand,
	Sobye babu sobye	I caught you from the jungle and tied you to the
	Hoy hoy janele, hoy hoy bujile	khemari
	Hoy hoy janele, hoy hoy bujile	Whatever be the words and work, they are good
	Jungle pora dhoi aani, khemari te	Babu yes, petting
	bhandi	Babu yes, petting
	Yeh naam hoy hoy , yeh kam hoy	Yes you know, yes you understand,
	hoy	You were in the mountains and ate bamboo
	Sobye babu sobye	shoots
	Sobye babu sobye	You came to the plain, and take food from your
	Hoy hoy janelé hoy hoy bujelé	pandhi
	Paharot asilé kako gaazali khai	There is water inhabited by microbes here
	Boyamoloy ahilé toy phandi theka	If you are kind, I will untied your legs
	khai
	Panit hace, panit kaora bhamot
	hace beang
	kosor mosor nokoriba meledia
	thang

Table 2 :

 2 Lyrics and transcript of the second chant.

	Slowly, slowly, move forward!
	Continue quietly, move forward!
	Again, again, move forward!
	Continue quietly, move forward!
	Slowly, slowly, move forward!
	Hoy hoy, you know, hoy hoy you
	understand,
	Our babu moves forward!
	Slowly, slowly, move forward!
	Oh stop! turn! turn!
	Oh Stop! ... move forward!
	Hoy hoy,
	Slowly, slowly, move forward!
	Continue straight, move forward!
	Softly, softly move forward!
	Quietly, quietly move forward!
	Slowly, slowly, move forward!
	Give your hears, open your eyes
	Slowly, slowly, move forward!
	Softly, softly move forward!
	Continue straight, move forward!
	Oh stop! turn! , turn!,
	Oh Stop! hoy hoy
	Give your hears, open your eyes

As for the first chant, I will now focus on the study of the second one. Haste haste agaat jah! Sida bhaté agaat jah! Diré diré agaat jah! Sida bhate agaat jah! Haste haste agaat jah! Hoy hoy janelé hoy hoy bujelé, Amar babu agaat jah! Haste haste agaat jah! Oh dhaat! soigum! soigum! Oh pishu! ... Agaat jah! Hoy hoy, Haste haste agaat jah! Sida maya agaat jah! Bate bate agaat jah! Dire dire agaat jah! Haste haste agaat jah! Kan melé enebi, suku mele sabi Haste haste agaat jah! Baté, baté agaat jah! Sida maya agaat jah! Ohh daat! soigum! soigum! Oh pishu! hoy hoy Kan melé enebi, suku melé sabi


Ndeye Gueye Lo

Jean-Marie Flaus
email: jean-marie.flaus@grenoble-inp.fr

Olivier Adrot
email: olivier.adrot@grenoble-inp.fr

Review of Machine Learning Approaches In Fault Diagnosis applied to IoT System

Keywords: fault detection and diagnosis, artificial intelligence, machine learning, hybrid system, Internet of Things

With increasing complex systems, low production costs, and changing technologies, for this reason, the automatic fault diagnosis using artificial intelligence (AI) techniques is more in more applied. In addition, with the emergence of the use of reconfigurable systems, AI can assist in self-maintenance of complex systems. The purpose of this article is to summarize the diagnosis research of systems using AI approaches and examine their application particularly in the field of diagnosis of complex systems. It covers articles published from 2002 to 2018 using Machine Learning tools for fault diagnosis in industrial systems.

I. INTRODUCTION

The current industrial trend regarding automatisms and regarding industrial plants leads us towards systems more and more complex mechatronics, working in an uncertain, evolutionary environment. It is so necessary to develop a diagnosis module to detect a fault (Fault Detection) that may affect the operation of these systems and to locate their causes (Fault Isolation). Therefore, a diagnosis module is needed to improve the performance and productivity of systems and limit the consequences of failures that can be catastrophic on human goods and life.

Diagnosis consists of detecting abnormal functioning from sensor data. These data may be noisy or corrupt due to unpredictable events. That abnormal operation may be a failure of process equipment (a sensor, actuator or a component), control system failure (due to operator error or cyber-attack of the system), or change of environment for example resources that are lacking (unavailable operators, exhausted stocks, etc.), or change due to non-conformity product etc. After detecting abnormal functioning, the cause can then be located and identified to make decisions (corrective actions or reconfiguration of the system). The different type of faults in the process is illustrated on the fig. 1.

In the literature, there are two main approaches for fault diagnosis, it is shown on the fig. 2. : approaches that use an analytic or physical model of the system [START_REF] Venkat Venkatasubramanian | A review of process fault detection and diagnosis Part I: Quantitative model-based methods[END_REF] and approaches that rely only on system observations [START_REF] Venkatasubramanian | A review of process fault detection and diagnosis part III: Process history based methods[END_REF]. The use of the techniques of diagnosis with models seems difficult and expensive, by offering less satisfactory performances. Besides, several industrial applications exist where a model is difficult even impossible to obtain due to increased complexity or several reconfigurations involved in the production process [START_REF] Najafi | Application of machine learning in the fault diagnostics of air handling units[END_REF]. For this type of industrial application, the only operational monitoring methods are those without analytic or physical models. Among these process history-based techniques, we have signal processing tools and AI tools. However, the approach of signal processing tools is limited to detection and have huge problems with false alarms because they use statistical tests (mean, or variance) to define detection thresholds [START_REF] Lo | Gear and bearings fault detection using motor current signature analysis[END_REF]. In fact, the artificial intelligence offers tools totally decoupled by the structure of the system, not requiring the preliminary modeling of the latter and allowing a real-time follow-up of its evolution. Besides, on-line reasoning makes that the approach of Artificial intelligence is stronger in changes in operating modes and have several reconfigurations. Manufacturing is a mature industry with machines and assembly lines that run with a high degree of automation. Supervisory Control & Data Acquisition Systems (SCADA) is prevalent industry standards and IoT is complementary to SCADA. SCADA's focus is on monitoring and control. IoT's focus is firmly on analyzing machine data to improve the productivity. SCADA systems consist of Programmable Logic Controllers (PLC) for interfacing with sensors in the machines. It is illustrated on the fig. 3. IoT and SCADA are complementary technologies for Industry 4.0. In the era of Industry 4.0, the Internet of Things (IoT) technologies is applied in many production systems. One of the important applications of IoT systems in the industry is monitoring, fault detection, and diagnosis of manufacturing systems [START_REF] Sciences | A systematic literature review in fault analysis for IoT Guru Prasad Bhandari * Ratneshwer Gupta[END_REF]. Most of the IoT systems involve many sensors that collect a huge quantity of data and machine learning (ML) methods are applied to analyze them for various goals [START_REF] Niggemann | Data-driven monitoring of cyberphysical systems leveraging on big data and the internet-of-things for diagnosis and control[END_REF]- [START_REF] Yen | A Framework for IoT-Based Monitoring and Diagnosis of Manufacturing Systems[END_REF]. ML can be defined as a field of study in computer science that enables personal computers to automatically get more efficient at a given task through experience [START_REF] Sharp | A survey of the advancing use and development of machine learning in smart manufacturing[END_REF]. In fact, the interesting use of machine learning tools is that they can process many data and are tools of decision-making support for fault diagnosis [START_REF] Ge | Data Mining and Analytics in the Process Industry: The Role of Machine Learning[END_REF].

The organization of this paper is as follows. After the introduction in Section I, some issues in fault diagnosis are presented in Section II. The machine learning approaches in fault diagnosis are reviewed in Section III. In Section IV we discuss the issues presented in the introduction. This paper is ended in Section V with a conclusion on the challenge of machine learning tools in the diagnosis and future research.

II. SOME ISSUES IN FAULT DIAGNOSIS

The main problem detection is the setting of a threshold. In fact, setting a low threshold for detection, generate many false alarms. These could disrupt the production system by causing production delays and affect planning and logistics. On the other hand, setting a very high detection threshold may result in the risk of not detecting a slow drift or serious situations. This may not be tolerated for operational safety and security issues. In this context, there are many uncertainties to consider: uncertainties related to lack of process knowledge, data uncertainties in sensors that may be noisy or missing data, uncertainties related to process variability.

In this context, where the system becomes very complex and it is hard to define an analytic model of the system, ML models are used to cope with this challenge. However, the main issues of ML tools are the data quality (missing or noisy data), the ability to consider several types of variables (discrete or continuous), the way to consider the time for the dynamic system and the ability to generalize in order the model has the capacity to take a good decision with a new observation.

III. MACHINE LEARNING APPROACHES

The application of IoT systems in industries creates a huge amount of data. In addition, these industrial systems have become more and more complex and it is difficult to obtain an analytical model of the system. In this context, the use of ML tools comes out obvious and logic to cope with the challenges of diagnosis in these systems. The goal of this paper is to show through several methods, the application of ML techniques on fault detection and diagnosis problems. Most of the publications reviewed and cited in this paper cover articles published from 2002 to 2018 using ML tools for fault diagnosis in industrial systems. ML methods are reviewed in the literature many times [START_REF] Ademujimi | A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis[END_REF]- [START_REF] Liu | Artificial intelligence for fault diagnosis of rotating machinery: A review[END_REF]. Therefore, we will review the most important and popular ML techniques in the diagnosis of a process in industries. Among the machine learning techniques, there are Support Vector Machine (SVM), Artificial Neural Network (ANN), Fuzzy Neural Network (FNN), Decision Trees (DT), Bayesian Network (BN).

A. Support Vector Machine

SVM [START_REF] Yin | Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes[END_REF] has an excellent performance in generalization with small training data, it is applied in an analog circuit fault diagnosis using wavelet transform as preprocessor with high accuracy in classification [START_REF] Long | Least Squares Support Vector Machine Based Analog-Circuit Fault Diagnosis Using Wavelet Transform as[END_REF]. The idea of using SVM for separating two classes is to find support vectors to define the bounding planes, in which the margin between both planes is maximized. The number of support vectors increases with the complexity of the problem. However, SVM is less good when you have more than ten classes. In fact, in [START_REF] Chiang | Fault diagnosis based on Fisher discriminant analysis and support vector machines[END_REF] proximal support vector machines (PSVM) reduce significant computation time and memory requirement in fault diagnosis on the Tennessee Eastman process (TEP) simulator when we have multiple fault classes. Recently, semi-supervised data-driven fault detection and diagnosis algorithm are ventilation air-conditioning (HVAC) faults acquiring only a few faulty training samples in [START_REF] Yan | Semi-supervised learning for early detection and diagnosis of various air handling unit faults[END_REF]. The proposed semi-supervised SVM solves the problem of not enough faulty training samples with high performance proposed to detect and diagnose the components of heating.

SVM tends to generalize well even with a limited amount of training data. The main challenge is to select the kernel function parameters is very difficult. In addition, it is less efficient in a case of lot of data, because number of support vectors increases with the complexity of the problem.

B. Artificial Neural Network

ANN are powerful tools with the ability to perform classification and prediction operations. ANN can be considered as weighted directed graphs, the neurons being the nodes, and the connections between the nodes being weighted links. This learning adjusts weights as well as activation functions to adopt the desired behavior. Two types of learning are used: supervised learning and unsupervised learning [START_REF] Fenton | Fault Diagnosis of Electronic Systems Using Intelligent Techniques : A Review[END_REF].

-Supervised learning: the aim is to determine synaptic weights from the tagged examples. Thus, the network parameters are modified to minimize the error between the target output (provided by the expert) and the actual output of the network. Backpropagation is an example of a supervised learning algorithm [START_REF] Sorsa | Neural Networks in Process Fault Diagnosis[END_REF].

-Unsupervised learning: the input data does not contain information about the desired output; the learning is carried out with rules that change the parameters of the network according to the input data. The associative memory learning algorithm for Hopfield Nets is an example [START_REF] Li | Multiple fault diagnosis of analog circuit using quantum hopfield neural network[END_REF].

In [START_REF] Li | Multiple fault diagnosis of analog circuit using quantum hopfield neural network[END_REF] quantum Hopfield Nets is used to diagnose the multiple faulty behaviors of analog circuits using probability mechanism. The fault patterns are then quantized and standardly orthogonalized to fed to the quantum Hopfield neural network. Using a quantum associative memory mechanism and quantum superposition principle, the systematic approach gives a good explanation of the probability of multiple faults. In [START_REF] Tayarani-Bathaie | Dynamic neural network-based fault diagnosis of gas turbine engines[END_REF], neural network-based fault detection and isolation (FDI) scheme is presented to detect and isolate faults in a highly nonlinear dynamics of an aircraft jet engine. The fault detection and isolation schemes consist of multiple ANN or parallel bank of filters, corresponding to various operating modes of the healthy and faulty engine conditions. Using the residuals that are generated by measuring the difference of each network output and the measured engine output various criteria are established for accomplishing the fault diagnosis task, that is addressing the problem of fault detection and isolation of the system components. A novel approach proposed in [START_REF] Hyun Cheol | Fault Detection and Isolation of Induction Motors Using Recurrent Neural Networks and Dynamic Bayesian Modeling[END_REF], is to create a neural model to emulate normal system behavior and additional models to emulate various fault conditions. The neural models are then placed in parallel with the system to be monitored, and fault detection is achieved by comparing the outputs of the neural models with the real system outputs. Fault classification is based on a simple threshold test of the residuals formed by subtracting each neural model output from the corresponding output of the real system. A Bayesian network is used to evaluate the residuals.

The power of ANN is their ability to approximate and recognize patterns. In diagnostic applications they have shown great promise in areas where noise and error are present. However, ANN is computationally intensive making convergence typically slow during training and prone to overfitting and requires a large diversified dataset for training to prevent this problem.

C. Fuzzy Neural Network

Diagnosis applications mainly include hybrid neuro-fuzzy models, which neural networks and fuzzy systems are combined homogeneously. The most common neuro-fuzzy systems are based on two types of models, the Tagaki Sugeno Kang (TSK) and Mamdani models [START_REF] Shihabudheen | Recent advances in neurofuzzy system: A survey[END_REF], combined with neural learning algorithms. The Neuro-Fuzzy classification (NEFCLASS) is also recognized for these classification capabilities [START_REF] Nauck | Generating classification rules with the neuro-fuzzy system NEFCLASS[END_REF]. There are two main uses of neuro-fuzzy networks in monitoring. These uses are most often based on the study of residues that are generated by the difference of an estimated signal given by a neuro-fuzzy observer with the actual values of the signal. These residues are then classified and evaluated by the neurofuzzy system.

In [START_REF] Palade | Fault diagnosis of an industrial gas turbine using neuro-fuzzy methods[END_REF], a neuro-fuzzy based learning and adaptation of TSK fuzzy models are used for residual generation, while for residual evaluation a neuro-fuzzy classifier for Mamdani models is used. The purpose is to detect and isolate faults to an industrial gas turbine, with emphasis on faults occurred in the actuator part of the gas turbine. An intelligent method is presented in [START_REF] Chen | Application of fault tree analysis and fuzzy neural networks to fault diagnosis in the internet of things (IoT) for aquaculture[END_REF] for fault diagnosis based on fault tree analysis and a fuzzy neural network in IoT equipment used for aquaculture. The fault tree presents a logic structure of fault symptoms and faults. Rules extracted from the fault trees avoid duplicate and redundancy. Moreover, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. The results show that this method can diagnose multiple faults with accuracy. Sequential Fuzzy Clustering Dynamic based Fuzzy Neural Networks is developed in [START_REF] Jahromi | Sequential fuzzy clustering based dynamic fuzzy neural network for fault diagnosis and prognosis[END_REF] and successfully applied for monitoring of a high-speed milling process. It can sequentially learn the model, adapt itself to variations, and provide an estimation or prediction on the status of the process. It facilitates for nonintrusive fault diagnosis.

FNN has both advantages of ANN and fuzzy logic. In fact, it has the capacity to represent inherent uncertainties of the human knowledge with linguistic variables and it is robustness in relation of the possible disturbances in the system. Nevertheless, FNN need to have some expertise on the system to establish rules and the training time are very computationally intensive.

D. Decision Trees

The DT is designed to classify or predict a discrete category from the data. In the machine learning sense, the goal is to create a classification model (classification tree) that predicts the value of a target variable by learning simple decision rules inferred from the data features [START_REF] Piechowiak | Intelligence artificielle et diagnostic[END_REF]. There exist several algorithms for training decision trees from data including ID3, C4.5, and CART. A novel mode estimation algorithm is developed in [START_REF] Zhao | Monitoring and fault diagnosis of hybrid systems[END_REF] that uses model-based prediction with Petri nets. And the diagnostic system traverses the decision-tree (ID3) and requests sensor tests as indicated by its nodes. The monitoring algorithm computes the deviations between the expected and the actual sensor values. This method is applied in a hybrid system, DC265 printer. The use of a decision tree offers efficiency; however, it limits the approach to the diagnosis of single faults. In [START_REF] Mylonas | Fault diagnosis of wind turbine structures using decision tree learning algorithms with big data[END_REF], the authors propose to propagate wind turbine telemetry through a decision tree learning algorithm to detect faults, damage, and abnormal operations. They train a set Bagged decision tree classifier on a dataset from an offshore wind farm comprising 48 wind turbines and use it to automatically extract paths linking excessive vibrations faults to their possible root causes. CART algorithm is used for decision tree induction to a diagnostic strategy for air handling units in [START_REF] Yan | A decision tree based data-driven diagnostic strategy for air handling units[END_REF]. A steady-state detector and a regression model are incorporated into the strategy to increase the interpretability of the diagnostic strategy developed. It is shown that this strategy can achieve good diagnostic performance. In [START_REF] Yang | Random forests classifier for machine fault diagnosis[END_REF], the proposed method is based on Random Forest (RF), a novel ensemble classifier that builds many decision trees to improve the single tree classifier. This method is applied to motor faults diagnosis and it gives a good accuracy of faults classification. In [START_REF] Yang | Sensor fault diagnosis based on on-line random forests[END_REF], they propose to use the on-line random forests (ORF) algorithm to identify sensor fault. The sample set is derived from Tennessee Eastman process. Experiment results show that the accuracy of ORF is almost the same as RF and ORF has better adaptability than RF.

DT are visually more intuitive, simpler and easier to assimilate and interpret by humans and engineers. Third, unlike other classification methods, with decision tree classifiers, one can perform data-driven root cause analysis of faults; one can trace a path from the end state to the initiating, a way that follows the sequence and chronology of how events are interlinked. They are very robust to noisy data and incomplete data. However, they need to use pruning parameter to reduce the overfitting.

E. Bayesian Network

BN is an important probabilistic graphical model, which can deal effectively with various uncertainty problems based on probabilistic information representation and inference. BN is a probabilistic graphical model representing a set of random variables and their conditional dependencies via a directed acyclic graph. Such a network consists of qualitative and quantitative parts. Qualitative part is directed acyclic graph, in which nodes represent system variables, whereas arcs symbolize dependencies or cause-and-effect relationships among variables. The quantitative part consists of the conditional probabilistic table, which represents the relationship between each node and its parents. Fault diagnosis procedures with BNs consist of BN structure modeling, BNs parameter modeling, BN inference, fault identification, and validation and verification. Several methods were reported in constructing BN structure models for fault diagnosis. Three main methods include cause-and-effect relationship, mapping algorithms, or structuring learning. Moreover, the parameters are the prior probability of root nodes and the conditional probability of leaf nodes. These probabilities can be achieved from expert knowledge and experience and statistical results of historical, simulated, and experimental data [START_REF] Cai | Bayesian Networks in Fault Diagnosis[END_REF]. In proposed algorithms are based on analyzing the observed behavior of the system and comparing it with a set of behavioral patterns generated based on various faulty conditions. They show how such a pattern matching problem can be formulated as an estimation of the posterior distribution of a Bayesian probabilistic model. When fault diagnosis is involved in temporal, system, or complex systems, inevitable difficulties with static BNs are observed. Therefore, some other types of BNs, such as Dynamic Bayesian Network (DBN) and Object-Oriented Bayesian Network (OOBN), are used to solve these problems. DBNs are extensional BNs with time-dependent variables and can be used to model the temporal evolution of dynamic systems. Thus, an approach relies on the use of Bayesian Network, which model the electrical power system to the arithmetic circuit, is proposed in [START_REF] Ricks | International Journal of Approximate Reasoning Diagnosis for uncertain , dynamic and hybrid domains using Bayesian networks and arithmetic circuits[END_REF]. This method can handle varying fault dynamic, fault progression and fault behavior cardinality. In [START_REF] Cai | A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering[END_REF], DBN are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, Transient Fault (TF), Intermittent Fault (IF), and permanent fault (PF). In addition, the fault diagnosis methodology can identify the faulty components and distinguish the fault types at a different time. OOBNs provide an approach to achieve hierarchical representation of the model, and each level corresponds to the level of abstraction, revealing encapsulated nodes for the current layer of an object. This approach reduces the complexity of building BNs and improves reusability of models.

A real-time fault diagnosis methodology of complex systems with repetitive structures is proposed using OOBN in [START_REF] Cai | A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks[END_REF]. Once faults occur, the proposed OOBN-based fault diagnosis system can report the faults and warnings. For a specified system with a certain situation, the operator can input some known experience information to the additional information layers of the additional information and common cause failure subnetworks.

BN is intuitively easy for the user to understand the interaction between the model variables. It is useful for modeling uncertainty and can be readily used to model hierarchical levels of multiple causes and effects with data from numerous sources, which is typically found in manufacturing systems. The main challenge of training a BN is in the construction of the tree structure and several methods including expert opinion have been proposed to this challenge.

IV. DISCUSSIONS

In the diagnosis process, the system evolves in a nondeterministic environment that requires considering uncertainties that are noisy measurements, missing data, lack of knowledge of the system and its variability. Generally, the good performance of a model of machine learning results mainly from the quality of the data used. The main challenge of diagnostic systems in industrial applications is the estimation of faults with incomplete observations. In industrial practice, it is necessary to deal with incomplete datasets and unknown measurements, while continuously demanding useful and reliable information to support decision-making. The complexity of this issue depends on the mechanism of missing data and the informative level of the process database [START_REF] Askarian | Fault diagnosis of chemical processes with incomplete observations: A comparative study[END_REF]. There are many approaches for solving these problems such as deletion of the incomplete or estimation of missing data. However, some ML tools can handle directly incomplete data. For example, BN could reason with incomplete observations. In fact, it uses probabilities to estimate our uncertainty degree and sometimes it uses expectation-maximization algorithms (EM) to learn parameters when it exists some missing data in the dataset [START_REF] Wang | An improved Bayesian network method for fault diagnosis[END_REF].

An important part of ML is the ability of the model to generalize. Indeed, a model that is too complex relative to the problem we want to model will learn from the noise included in the dataset; therefore, the model may be overfitting, unable to generalize, and do many errors with a new observation. For this reason, in certain methods, pruning techniques are used, for example, decision trees and neural networks. The goal of pruning techniques is to stop the training to avoid overfitting. In addition, most of these methods are supervised techniques that use the expert's knowledge to make the diagnosis. The major drawback of supervised techniques is that the model only knows the type of faults it has learned. Nevertheless, with a new fault, some models can do ambiguity rejection or distance rejection to fill the lack of knowledge of the system and to adapt to the system evolution (useful for multiple reconfiguration systems).

Furthermore, in the diagnosis process, the sensors generate numerical data, and not all ML tools use directly the numerical data. However, most machine learning techniques work only with continuous variables, for example, SVM, and ANN. On the other hand, other techniques use discrete variables; it is the case of DT and BN. In DT, C4.5 supports numerical target variables. For the other algorithms like ID3 and CART, they use discrete variables or convert the continuous variables into the interval. BN use usually discrete random variable and sometimes it is possible to incorporate random continuous variable if it has a Gaussian probability distribution [START_REF] Chen | Learning Discrete Bayesian Networks from Continuous Data[END_REF]. FNN is the only technique that could handle various type of data (numeric and symbolic). In fact, it converts quantitative and qualitative values into a fuzzy set. The optimal diagnosis process is to build a model ability to process numerical or symbolic data where the operator may incorporate his knowledge by improving the diagnosis. Finally, we summarize the discussion given the table I below that allows comparing techniques according to the type of variables used. Another issue in the diagnosis process is the setting of the detection threshold. For example, with BN, some researchers rely on the posterior probability of the fault node. Usually, the probability of occurrence of an event is high if its posterior probability increases. However, this way can lead to errors results as some failures may have high probabilities inherently before inference. To avoid this issue, some use fuzzy logic to reduce false alarm rates, for example, the FNN [START_REF] Alshammari | Using neuro-fuzzy approach to reduce false positive alerts[END_REF] or establish a set of rules based on probability in case of BN [START_REF] Cai | Bayesian Networks in Fault Diagnosis[END_REF].

Variability of the process is useful to consider in fault diagnosis. In fact, the complex or hybrid systems are characterized by multiple reconfigurations, several operating modes, and the number of sensors changes regularly particularly in IoT Systems: addition or deletion of one or more sensors. The diagnosis of Hybrid Dynamic Systems (HDS) requires the joint exploitation of continuous dynamics and discrete dynamics [START_REF] Mouchaweh | Diagnostic des Systèmes dynamiques hybrides ( SDH )[END_REF]. In this case, to diagnose in this context, we will need robust tools with the change of models and measurement system. BN is usually applied in this type of systems in the literature [START_REF] Zhou | A Bayesian framework for fault diagnosis of hybrid linear systems[END_REF].

V. CONCLUSION This review of machine learning application in fault diagnosis demonstrates that ML techniques could be a very useful tool in fault detection and diagnosis. This study shows that a single method does not allow filling all the characteristics that one seeks in the diagnosis of a system. Some methods can complement others to have a better diagnosis system. In era industry 4.0, with the internet of things, sensors generate enormous volume, variety, and velocity of the data. Moreover, it is necessary to design a cloud for data storage. However, there are some issues such as security and scalability. Thus, it is important to build a system of diagnosis with the ability to detect and identify the fault due to a cyber-attack and the fault due to failure on the system. In the future, we will develop ML tools able to make a robust diagnosis and to cope with this challenge.

Fig. 1 .

 1 Fig. 1. Possible faults in the Cyber-Physical System

Fig. 2 .

 2 Fig. 2. The different methods in fault diagnosis

Fig. 3 .

 3 Fig. 3. The different layers in SCADA system

TABLE I .

 I COMPARISON OF TECHNIQUES ACCORDING TO SOME CRITERIA

ACKNOWLEDGMENT

The authors would like to acknowledge the comments and suggestions of the various reviewers that have been incorporated in this revised manuscript.


domain: spi phys

text:
Marina Olazabal-Loumé

François Chedevergne

Florian Danvin

Julien Mathiaud

Roughness corrections applied to the simulation of turbulent hypersonic flows

Roughness corrections applied to the simulation of turbulent hypersonic flows

Introduction

Heating during a reentry flight is known to cause rough surface state on vehicles. In the low atmospheric layers, roughness effects can play an important role on the turbulent flow, increasing the friction and heat fluxes on the walls. Consequently, simulations have to account for these effects and usually, for industrial applications, turbulence is modelled using RANS (Reynolds Averaged Navier-Stokes) methods. Different approaches can deal with roughness effects. The direct numerical simulation (D.N.S.) consists in resolving the whole real geometry including rough elements. Although the accuracy of the simulation is very high, the computational cost can be prohibiting for complex and large size geometries. An alternative method, the discrete element method, is based on the inclusion of corrective terms into the Navier-Stokes or boundary layer equations. This method includes a blockage coefficient, i.e. the ratio between the volume accessible by the fluid over the total volume, and accounts for the drag and heat transfer induced by roughness elements. The discrete element method has been mostly developed to describe the interaction between distributed roughness patterns and the flow. Nevertheless, it can be considered as quite intrusive for CFD solvers due to significant modifications of the original equations. The last approach, introducing the concept of equivalent sand-grain, consists in bringing any kind of roughness to an equivalent sand-grain height that will produce the same friction increase. This approach has been widely used for industrial purposes. Several formulations were developed to extend RANS turbulence models to rough surfaces. Such corrections for the k-ω SST model were recently proposed [2-3] by Aupoix to include roughness effects on both friction and thermal fluxes. The corrections were validated on low Mach number experimental data. After a first validation step in hypersonic conditions [4], this paper presents two original applications of these corrections to hypersonic flows. In addition, a benchmark between two different RANS solvers is performed.

Turbulent flow modelling on a rough wall

Equivalent sand grain approach

The pioneer and reference work of Nikuradse [5] exploring the effect of sand grains on pressure losses in cylindrical pipes, has led to the concept of equivalent sand grain height . The roughness influence on the flow field was found to depend on the non-dimensional height , where , is the friction velocity. Three different regimes were identified:

 Hydraulically smooth ( ): the roughness has no influence on the flow field.

 Transient (

): the drag is generated both by viscous forces and by the pressure exerting on the roughness elements.  Fully rough ( : the skin friction increase is independent of the Reynolds number. Viscous effects become negligible compared to pressure efforts.

The different bounds can differ according to authors. Schlichting [6] proposed for the completely rough regime to assimilate any kind of roughness to an equivalent sand grain (of height k s ), which would generate the same skin friction increase as in Nikuradse's experiments. The importance of both the roughness element form and the density of the distribution were pointed out. Several correlations were proposed to evaluate the equivalent sand grain height [7][8].This approach is advantageous thanks to its ease of implementation and its weak additional computational cost. Nevertheless, the method is quite sensitive to the equivalent sand-grain height estimate.

Roughness effect on the turbulent boundary layer

Velocity variations inside the boundary layer are usually studied defining the following non-dimensional variables , .

In eddy viscosity RANS models, the total shear and heat flux are given by , and , where and are the eddy viscosity and conductivity respectively. For a smooth flat plate without pressure gradient, the turbulent boundary layer profile (corresponding to the solid line on Figure 1) can be decomposed into three different regions [9]. In the viscous sublayer, where the viscous forces prevail due to the no-slip condition at the wall, the velocity profile can be approximated by for y + < 11. Above, the log layer corresponds to a balance between inertial and viscous forces where turbulence develops.The velocity profile follows the so-called logarithmic law In Grigson's study [10], a formulation was proposed for in order to fit .

The k-ω SST model for turbulent compressible flows

Menter's model

The k-ω SST model was developed by Menter [1] to mitigate some lack in the framework of two-equation turbulence models. It combines the suitability of to capture the near wall turbulent flow and the k- model properties for free flows. This is achieved using a blending function so that the compressible equations of kinetic turbulent energy and specific dissipation rate conservation write respectively

Corrections for compressible flows

Most of turbulent models are obtained neglecting the compressibility terms in the turbulent kinetic energy k equation. These terms are mainly related to pressure fluctuations and dilatation dissipation. These effects were studied by different authors as Sarkar [14] or Zeman [15]. Main results were summarized by Rumsey [16]. Different contributions were proposed to include the compressibility effect on turbulent flows, based on the turbulent Mach number parameter where a denotes the speed of sound. Sarkar and Zeman proposed different formulations of a pressure-dilatation correlation term. In the case of an homogeneous shear flow, this distribution of the energy due to pressure fluctuations can be expressed as a correction of the production and dissipation terms in the kinetic energy equation writing where and . In this work, we use this last formulation combined with the k-ω SST model. The main effect of this correction is to lower the turbulent energy exchange close to the wall where skin friction and heat flux may be over-estimated.

Roughness corrections for the k-ω SST model

4.1 Dynamic correction Aupoix [2] developed two versions of a roughness correction adapted to the k-ω SST model in order to improve the fully rough regime. Here, we retained finally, the correction relying on Colebrook's data. The correction consists in boundary condition modifications for k and ω. The velocity shift is recovered thanks to an artificial increase of the turbulence level at the wall. The modifications of the wall turbulent scalars for the k-ω SST model write , , ,

The dimensional boundary conditions are obtained using , .

Thermal correction

The Reynolds analogy does not hold for rough walls necessitating an additional thermal correction. Aupoix [3] suggested to account for this phenomenon by modifying the turbulent Prandtl number. Based on predictions obtained using the discrete element method, the turbulent Prandtl number correction reads

This correction lowers the effect of the eddy viscosity on the turbulent conductivity since the friction increase due to roughness is larger than the increase of the heat transfer. The turbulent Prandlt number correction depends on the equivalent sand grain height (k s ), on the effective roughness height (h), and on the corrected wetted surface ratio (S corr ) , , where , . McClain [18] defines the mean elevation as the base surface one would obtain, if the roughness elements were locally melted. From this new reference surface, only the exceeding roughness parts are taken into account to define the effective element diameter and height and the new distance between two elements. These results were extended to the equivalent sand grain approach and to the discrete element method. The process of evaluating the corrected wetted surface ratio S corr derives from these definitions. It corresponds to the ratio of the elevated wetted surface (including new defined roughness elements) over the smooth surface at the bottom of the original elements. The corrected wetted surface ratio S corr corresponds to the wetted surface of the roughness above the mean elevation. The mean elevation concept was introduced by McClain [18] to account for the presence of dead water zone between roughness in dense configurations. The mean elevation can be estimated using the melt-down surface. More details can be found in [3].

Simulation of Holden's experiments

Experimental conditions

Experiments on rough models in hypersonic conditions were reported by . Two configurations are studied in the present paper: a 6° half angle slender cone and a biconic model with a 45° half-angle conic part where the roughness elements are located. Both experiments were conducted in the CALSPAN hypersonic facility with identical freestream flow conditions: , m/s, 1454.8 Pa, 60 K while the wall temperature is 318.4 K. Among the several test conditions, only cases with 0° incidence are retained. Holden [24] estimated experimental error bars of 20%.

In the following, results are presented using international coordinate system except for length for which inch unit is used. The experimental configurations are simulated using a multi-blocking structured code solving RANS equations and already used in a previous study on hypersonic flows [4]. It is based on a finite volume approach and a second order scheme. The code, developed at CEA, will be referenced as "NS" in the following.

Slender cone

First, the 6° half-angle cone is considered. Concerning this experiment, Holden indicated that a laminarturbulent transition occurs on the smooth cone while no indication is given for the rough cone case. Thus, simulations are performed imposing a transition location for the smooth wall case and a fully turbulent regime for the rough wall case. Air is considered as an ideal gas in these simulations, the temperature variations being small. Skin friction and Stanton number measurements were acquired. They are respectively defined by , and , with the friction temperature , where is the recovery factor and is the adiabatic index. This configuration corresponds to edge boundary layer conditions of and K in simulations. The position transition in the simulation is imposed at the curvilinear abscissa s inch and is related to the location of a junction mentioned in Holden's publications.

Smooth wall

The final mesh was obtained after a convergence study with different refinements. The mesh refinement was found to be more restrictive for the laminar and transitional upstream solution. As mentioned above, the transition point location is not modelled but imposed in the input data. The evolution of the transitional zone is driven by the turbulent viscosity via the production and dissipation terms in k and ω transport equations. With such an abrupt transition without intermittency, the extend of the transition zone can be sensitive to the mesh refinement. Additionally, a slight overshoot of the turbulence level is also observed after the transition compared to a full turbulent computation. Rapidly this overshoot is damped and the results become superposed in the downstream region for types of computations. This will be illustrated in sub-section 5.3. As expected, we noticed a decrease of the skin friction and heat flux levels using Sarkar's correction of at most 20%. Figure 2 presents the friction coefficient and the Stanton number obtained with Sarkar's correction in the smooth configuration. Compared to experiment data, a good agreement is obtained. This correction appears necessary to retrieve a correct level of turbulence in the simulations and thus will be used in the following.

Rough wall

Different types of roughness elements were used for this configuration. To evaluate the equivalent sand grain height, we refer to where averaged roughness patterns were defined to analyse Holden's and Hill's [28] hypersonic experimental data. They are based on profilometer measurements and on the assumption of identical roughness elements with uniform density to be suitable to the discrete element type method. In the present study, these averaged patterns are used to evaluate the equivalent sand grain height, the effective roughness height and the corrected wetted surface ratio required by the roughness corrections presented in section 4. The averaged roughness height is k = 10 mils [27]. Moreover, Dirling's correlation is used to estimate the equivalent sand grain height, i.e. with where l is the average roughness element spacing, is the winward surface wetted surface and is the frontal area roughness element.

Benchmark step

In this study, results from ONERA's code CEDRE [29] are used in a benchmark analysis. The code can deal with any type of unstructured mesh and uses a second order MUSCL scheme. Figure 4 shows the comparison of simulations and experiment data on friction coefficients and Stanton numbers obtained for the smooth wall case. In the CEDRE code, the transition location is imposed directly by vanishing the eddy viscosity in the upstream region. This slightly differs from the method used in the structured NS code but with a quite equivalent result. Simulations are in a good agreement in the laminar and in the turbulent zones. Figure 5 presents the computations using the roughness corrections. Both codes give the same general behaviour compared to the experiment data in particular by providing a similar mean level of the Stanton number using a constant roughness height along the model. Furthermore, simulations (not showed here) performed with the CEDRE code with a decreasing equivalent sand grain height value from the upstream to the downstream part evidenced a behaviour similar to the experimental data. Further investigations may be pursued to evaluate if some additional mechanism during the experiments could induce a modified equivalent roughness pattern viewed by the flow or if some additional modelling should be considered. The second model consists in a biconic geometry but only the first conic part of 45° half angle is of interest. Indeed, experimental data only concerns this first part of the model and provide heat transfers measurements. Simulations were performed with ideal gas and real gas equation of state. Presented results correspond to the second case where, due to the angle, the Mach number behind the shock falls to 1.86 while the temperature reaches about 940 K. Figure 6 shows experimental data on the smooth wall compared to NS simulations. The recovery factor used in the Stanton number evaluation is again . The dashed line corresponds to the fully turbulent computation and the solid line to the case where the transition is imposed. As mentioned in the previous sub-section, transition induces a slight overshoot but further downstream identical levels are obtained between the two computations. Simulation and experimental data are found to be in good agreement in both the laminar and the turbulent parts of the flow.

Rough wall

Finson's profilometer measurements allow to define a generic roughness element with a 4 mils height. Using Dirling's correlation, a conical approximation of the roughness element leads to the estimation of the equivalent sand grain and the corrected wetted surface ratio. Figure 7 presents the Stanton number distribution for experimental data and both the NS and CEDRE code results. The maximum gap between simulation and experiment lies between the error bars provided by Holden.

Conclusion

This paper presents original comparisons between experiments and numerical simulations in two different hypersonic configurations of turbulent flows on smooth and rough walls. RANS simulations are performed using the k- SST model coupled with the Sarkar compressible correction. Roughness corrections including both dynamic and thermal contributions complete the computational methodology. The first experimental configuration on a slender cone for which friction coefficients and Stanton numbers are available, permit to validate the adopted methodology. Two different codes, structured and unstructured, provide similar results. The friction increase so as the heat transfer increase, due to roughness is well reproduced and a good agreement is observed with measurements. Although the experimental friction coefficient and Stanton number distributions exhibit a slight decreasing trend on the rough case that is not recovered by the computations, the main effects of roughness are satisfactorily captured by the present methodology involving the roughness corrections developed by . It is worth mentioning that these roughness corrections were developed for incompressible boundary layer flows. Their applicability to compressible flows, and in particular in the hypersonic regime, was never demonstrated before. This observation is strengthened by the second experimental configuration consisting in a biconic model. The numerical results are also in good agreement with the measured Stanton numbers for both codes. In this configuration, a strong detached shock occurs leading to high compressible effects and which do not prevent the present methodology to reproduce the roughness effects on heat fluxes. The results from this study must now be completed to extend the validation range and shed on light on the residual discrepancies between computational results and experimental data even if existing experiments allowing the validation of roughness models on hypersonic configurations are still rare.

Figure 1 :

 1 Figure 1: Turbulent boundary layer velocity profile in wall coordinates (logarithmic scale for y+) : smooth wall (solid line) and rough wall (dashed line)

  (Shear Stress Transport) limitation of the eddy viscosity is introduced, it reads , norm of the mean rotation tensor. Boundary conditions at the wall are , .

Figure 2 :

 2 Figure 2: Comparison of simulations and experimental data: friction coefficient and Stanton number on the smooth wall

Figure 3

 3 Figure 3 presents the friction coefficient and the Stanton number obtained in experiments and the NS simulations. Comparisons are in a reasonable agreement, taking into account the experimental error bars. Measured friction level is found to be quite low at the end of the cone, and is comparable to the experimental data corresponding to the smooth wall. A similar decrease of the Stanton number is observed in the experiments. If NS simulations provides satisfactory mean values of and , the decreasing trend is not well reproduced.

Figure 3 :

 3 Figure 3: Comparison of simulations and experimental data: friction coefficient and Stanton number on the rough wall

Figure 4 :Figure 5 :

 45 Figure 4: Comparison of simulations for two different codes and experimental data: friction coefficient and Stanton number obtained on the smooth wall

Figure 6 :

 6 Figure 6: Comparison of simulations and experimental data: Stanton number obtained on the smooth wall

Figure 7 :

 7 Figure 7: Comparaison of simulations and experimental data: Stanton number obtained on the rough wall