Jaime Pando-Acedo

Enrique Romero-Cadaval

Consuelo Gragera-Peña

María Isabel Milanés-Montero

Noise, Vibration and Harshness on a Permanent Magnet Synchronous Motor for a Remote Laboratory

Keywords: harshness, noise, NVH, PMSM, remote, vibrations

The study of Noise, Vibration and Harshness (NVH) is becoming a key element when it comes to design and maintain a system that have rotatory elements. The understanding of the source of the vibrations can lead to a way of mitigating them, thus ensuring a better operation and expanding the life of the components. In this work, an analysis of the vibrations' frequency spectra of an electric drive is developed, extracting some conclusions from it and deducing a source. The possibility of operating this system remotely makes it a perfect experiment for a remote lab, operating through Internet, being a novelty for this kind of studies.

Introduction

Rotating machines have vibrations associated with its functioning, though it does not mean any abnormal operation. If we particularize for the case of an electric drive, the vibrations' sources may come from the mechanical part of the system (construction, misalignments, unbalance load, design flaws, etc) or the electric part (harmonics, unbalanced voltages, voltage gaps, etc). Noise, vibration and harshness (NVH) studies' aim is to identify those sources and propose solutions [START_REF] Putri | Application of Sinusoidal Field Pole in a Permanent-Magnet Synchronous Machine to Improve the NVH Behavior Considering the MTPA and MTPV Operation Area[END_REF][START_REF] Chauvicourt | Influence of Rotor Geometry on NVH Behavior of Synchronous Reluctance Machine[END_REF][START_REF] Tong | Vertical Vibration Analysis on Electric Vehicle with Suspended In-wheel Motor Drives[END_REF][START_REF] Pyrhönen | Advanced Measurement of Rotor Vibration in Electric Drives[END_REF].

This work uses the advantages of a rapid prototyping platform and the ability to remotely control an electric motor in order to develop the bases for a remote controlled laboratory. This will allow users to monitor and experiment with the machine through the internet, for research and educational purposes [START_REF] Langmann | Generic Remote Labs in Automation Engineering[END_REF][START_REF] Kruse | Remote Labs in ELLI[END_REF]. The analysis of the vibration spectra plays a key role in the NVH studies, which are especially important for the design and control of electric vehicles. This work is proposed as a first step towards the implementation of active control techniques, like active damping, suitable for the cancellation of vibrations from different sources.

This paper is schematized as follows. For a first section, the NVH techniques available will be discussed and a suitable one for this project will be selected. Then, the control employed for the system, which will use the Field Oriented Control [START_REF] Miñambres-Marcos | Issues and improvements of hardware/software co-design sensorless implementation in a permanent magnet synchronous motor using Veristand[END_REF] and Pulse Modulating Techniques, will be described. The acquisition of the data and processing will follow, finishing with an analysis of the results obtained and drawing some conclusions.

Relationship to Smart Systems

In the days we live in, information can be accessed almost anywhere. From the pc, laptop, smartphone, tablet or even wearable devices; all you need is an internet connection. In this sense, this work aims the possibility of remotely supervising or even control a research laboratory. In Fig. 1, a scheme for this kind of application is shown. The user has an application window from which he can monitor and control the system via the internet. The system connects to the internet using the TCP/IP protocol, and it consist of the control platform, an IGBT inverter and the electric drive. The control platform receives the orders from the user and generate the control signals for the inverter, as well as collects data from the inverter itself and the motor. A camera could be used for supervision of the hardware platform in anytime.

The remote laboratory represents a good implementation of a smart system, since it can be applied to industry besides research and education. This means that once it is fully developed, new UIs for controlling the system can be programmed using a web interface, for instance.

NVH and Control Algorithm

There are three groups in which NVH techniques can be classified into: numeric, experimental and analytic. Each of them have its advantages and disadvantages, and one should decide which method suits better for the application. For instance, analytic methods are useful for a first understanding of the problem and modeling. However, due to the increase in microprocessors' speed, numeric methods are getting more powerful and widely used every day. In the particular case of NVH, there are several numeric methods used, like the Finite Elements Method (FEM) or the Boundary Elements Method (BEM) [START_REF] Dos Santos | Multiphysics thermal and NVH modeling: Integrated simulation of a switched reluctance motor drivetrain for an electric vehicle[END_REF].

On the other hand, although it cannot give solutions in design phase because they can only be used with prototypes, experimental methods are useful when it comes to ease of realization, especially if you also use rapid prototyping. The data can be simply obtained using transductors, for later analysis using Fourier for studying the spectra.

This work aims the last type of the above, the experimental method. For conducting this kind of technique, the control platform used is dSPACE, from MATLAB, which allows remote controlling via Ethernet. The control algorithm is first elaborated and tested in simulations in Simulink, then is compiled and dumped in the dSPACE platform. The whole scheme of functioning can be observed in Fig. 2.

Control Meassures

Resolver adaptation stage

U DC i a i b ω θ ω ref , k p , k i PWM d a d b d c
To inverter

MATLAB control dumped in dSPACE

To dSPACE s a s b s c

ω mea , i abc ,v abc ,... TCP/IP User interface ControlDesk Fig. 2. Control block diagram. The block "Measures" takes the voltages, currents and speed values from sensors. "Resolver adaptation stage" block takes the information from the resolver and outputs speed and position. "Control" inputs all values from sensors and outputs the duty cycles for the PWM block. UDC, ia, ib, ω and θ are the bus voltage, motor currents, speed and position, respectively. Ωref, kp, and ki are the speed reference, and controller gains selected by user. The variables dabc are the duty cycles and s abc the switching signals.

The control strategy, a FOC strategy, is depicted in Fig. 3. The measured currents in stationary reference frame are inputs to the system, as well as the motor speed and DC bus voltage. The controller uses these inputs for generating the duty cycles that ensures that the references are met.

Experimental NVH Analysis

In this section the physical system will be described, and the later on the process for filtering the data obtained will be explained. For the last part, the results will be presented and some conclusions drawn.

Test Bench

In order to carry out the NVH study, a test bench consisting of a PMSM, an IGBT inverter, the dSPACE platform and a PC running MATLAB Simulink is employed. The electric drive is equipped with a resolver for speed measuring, for the obtaining of the current and voltage values, transducers are used. For the acquisition of vibration data, a piezoelectric accelerometer is employed and it stored for later processing in the PC. The PMSM properties are displayed in Table 1. As was stated previously, a piezoelectric accelerometer has been used for the acquisition of the vibration data, its position is showed in Fig. 4.

ControlDesk

The application interface presented to the user is elaborated using ControlDesk. This software allows the user to both monitor and control the dSPACE platform, been able to change control variables without the need of recompiling. One of the biggest advantages of this rapid prototyping tool is that it lets you change parameters of the control while executing it, thus making the tuning much faster.

Analysis of the Results

Once the vibration data is obtained and processed, one is interested in studying the low frequency amplitudes, since these are the one associated to vibrations. The high frequency amplitudes may have different causes, like electromagnetic noise, switching-frequency noise, etc. Therefore, a filter has been employed for removing the latter.

The filter proposed is a moving average, which formula is presented in [START_REF] Putri | Application of Sinusoidal Field Pole in a Permanent-Magnet Synchronous Machine to Improve the NVH Behavior Considering the MTPA and MTPV Operation Area[END_REF]. Lowering the window size M of the average lowers the cut-off frequency, eliminating the high frequency content.

n i nM n p P M    (1)
Since the frequency content we are interested in is located below 250 Hz, different values of the window size have been tried out until the high frequency content is eliminated. The raw data is shown in Fig. 5 a), with its frequency spectra (b)), and then the filtered data and its frequency spectra (Fig. 5 c) and d)). The speed equation of the PMSM is shown in [START_REF] Chauvicourt | Influence of Rotor Geometry on NVH Behavior of Synchronous Reluctance Machine[END_REF], where nm the motor rpm, f is the frequency of the inverter's voltage and p are the pole pairs.

; 60

mm p n f f n p  (2)
The PMSM has 4 pole pairs (Table 1), so if the motor is running at 300 RPM, the main frequency would be 20 Hz, the second harmonic 40 Hz, and so on. The data obtained shows this behavior exactly (Fig. 6).

The results show a relationship with the motor speed that is associated with misalignments in the rotor axis. This causes that the drive has to overcome an extra load at a determined angle, producing the vibration spectra observed.

Conclusions

Experimental analysis for a NVH study has been the method selected and the data obtained processed. The control employed for this work uses a the FOC technique and a PWM algorithm for controlling the inverter which feeds the PMSM. The platform used, dSPACE, allows the user to remotely control the system via TCP/IP protocol and the user interface, ControlDesk, to take advantage of the rapid prototyping. The processing of the data is necessary for the elimination of the high frequency noise.

Using Fourier analysis, a cause for the vibration spectra is deduced: rotor misalignment. For future works, the authors would like to go deeper into the frequency spectra trying to stablish more causes for the vibrations as well as associating the vibrations' amplitude values with the motor speed.

Fig. 1 .

 1 Fig.1. Scheme of a remote laboratory, allowing researchers and students to access and remotely control the system via the internet using protocol TCP/IP.

Fig. 3 .

 3 Fig. 3. Scheme of the FOC control used for the inverter-PMSM system. θ is the rotor position, p are the pole pairs and α the electrical angle.

Fig. 4 .

 4 Fig. 4. Transversal vibrations measuring using an accelerometer.

Fig. 5 .

 5 Fig. 5. Vibration data. a) Raw data. b) Fourier analysis of the raw data. c) Moving average with window size of 5 points. d) Fourier analysis of the filtered data.

Fig. 6 .

 6 Fig. 6. Peaks separating according to motor speed. (a) Motor speed: 300 RPM. (b) Motor speed: 500 RPM.

Table 1 .

 1 PMSM properties.

	Variable	Value	Units
	Pole pairs (p)	4	-
	Stator windings resistance (R)	0.2	Ω
	Rotor inertia (J)	3.6	Kg•m 2 •10 -3
	d-axis inductance (Ld)	1.871•10 -3	H
	q-axis inductance (Lq)	1.616•10 -3	H
	Magnet flux linkage	0.1323	V•s•rad

Acknowledgements. This work was supported by the Junta de Extremadura, under grant GR15177.


Ricardo Madeira
email: r.madeira@campus.fct.unl.pt

Nuno Paulino
email: nunop@uninova.pt.

Design Methodology for an All CMOS Bandgap Voltage Reference Circuit

Keywords: Low-voltage CMOS Bandgap Voltage Reference, Analog Design Methodology, Weak Inversion Operation

The Internet of Thing (IoT) has given rise to the integration of smart systems in the industrial, healthcare, and social environments. These smart systems are often implemented by system-on-chip (SoC) solutions that require power management units, sensors, signal processors, and wireless interfaces. Hence, an independent voltage reference circuit is crucial for obtaining accurate measurements from the sensors and for the proper operation of the SoC. Making, bandgap circuits indispensable in these types of applications. Typically, bandgap circuits are implemented using bipolar transistors to generate two voltages with opposite temperature coefficients (Complementary To Absolute Temperature -CTAT; Proportional do Absolute Temperature -PTAT) which are added resulting in a temperature independent voltage reference. The disadvantage of using bipolar devices is that the power supply voltage must be larger than the ON base-emitter voltage, resulting in voltages larger than 0.7 V. The low power demands of IoT and of technology scaling, have forced lower values for the power supply voltage and thus new bandgap circuits using only CMOS transistors have gathered increased interest. In these, the MOS transistors operate in the weak inversion region where its current has an exponential relation with its gate-to-source voltage, as in the bipolar devices. Making it possible to generate both the PTAT and the CTAT voltages and thus produce a temperature independent voltage reference. This paper describes a design methodology of an all CMOS bandgap voltage reference circuit, in which one of the transistors works in the moderate region and the other in the weak inversion, to achieve the lowest possible voltage variation with temperature. The circuit produces a voltage reference of 0.45 V from a minimum power supply voltage of 0.6 V, with a total variation of 1.54 mV, over a temperature range of -40 to 100 ºC, resulting in a temperature coefficient of 24 ppm/ºC, and a power supply rejection ratio (PSRR) of -40 dB.

Introduction

Reference voltage generators are required in almost any Internet of Thing (IoT) System on Chip (SoC). To ensure the proper behavior of these systems, these reference voltages should be stable, independently of variations in: environment temperature, supply voltage, and fabrication process. The bandgap reference (BGR) is the most commonly voltage reference generator used [START_REF] Razavi | The Bandgap Reference [A Circuit for All Seasons[END_REF]. Typically, a bandgap produces a constant reference voltage by summing two temperature dependent voltages -one Proportional to Absolute Temperature (PTAT) and the other Complementary to Absolute Temperature (CTAT). Most of the bandgaps uses the of a bipolar transistor to generate both CTAT and PTAT voltages [START_REF] Razavi | The Bandgap Reference [A Circuit for All Seasons[END_REF][2] [START_REF] Jiang | A low voltage low 1/f noise CMOS bandgap reference[END_REF]. However, due to the technology scaling and the need to reduce power consumption, the supply voltage has been greatly reduced. At supply voltages bellow 1 V, bipolar transistors are not suitable since they need a minimum value of 0.7 V to be turned ON. Therefore, to keep pace with this decrease in the power supply several all CMOS bandgap reference voltage circuits have been proposed [START_REF] Doyle | A CMOS subbandgap reference circuit with 1-v power supply voltage[END_REF][5][6][7] [START_REF] Lin | A 3μW, 0.65V regulator with an embedded temperature compensated voltage reference[END_REF]. In these new topologies, the PTAT and CTAT voltages are generated by using a MOS transistor in the weak inversion (or subthreshold region). In this region the transistor current depends exponentially on the gate-to-source voltage ( ), similar to the bipolar transistor. This paper describes a complete design methodology and simulation test of an all CMOS bandgap, in order to achieve the best performance.

Relationship to Smart Systems

The smart systems together with IoT play an important role on the new generation of information technology and it is predicted to have a major impact in the human life in the near future. This relatively recent technology aims to enable things to be connected anytime, anywhere, with anything and anyone ideally using any path/network or service. Such systems must be small, low power, and durable, hence they are often implemented by SoC solutions, that contain power management units, sensors, signal processors, and wireless transceivers. All this circuits and sensor require constant reference voltages so that they can work properly and obtain accurate and precise measurements. Moreover, since smart systems can be placed in any kind of environment, these reference voltages must be stable and robust even in harsh environments where the temperature variation can be high. Fig. 1 shows the simplified schematic of the all CMOS bandgap voltage reference circuit [START_REF] Quendera | A low voltage low power temperature sensor using a 2nd order delta-sigma modulator[END_REF], where the operational amplifier was implemented using a simple two stage miller compensated amplifier. The diode connected transistors operate in the weak inversion region where their current varies exponentially with . Thus, is given by ( 1) where is the technology current, is the substrate value, is the thermal voltage, and are the transistor length and width, and is the threshold voltage. For the 130 nm CMOS technology the value of and for an NMOS transistor were determined by simulation and are A, and 1.17, respectively.

Proposed Circuit and Design Methodology

(

) 1
The transistors along with the resistances are responsible for the generation of a PTAT and CTAT current. The values of these currents can be derived by applying KCL to node n1 [START_REF] Pereira | A 1.1 µA voltage reference circuit with high PSRR and temperature compensation[END_REF], where it is assumed that the amplifier gain is high so that , and that the current is equal in all the four branches of the circuit and so it can be derived through Ohm's law (3). The sizing of the resistors was performed using the same method used in [START_REF] Pereira | A 1.1 µA voltage reference circuit with high PSRR and temperature compensation[END_REF]. Where the total resistance value was set according with the area constraints which resulted in a total resistance ( ) value of M . Then, at room temperature ( ), m mV, V, V, (for employing common centroid techniques), then

Simulation Results

In the theoretical analysis, it was assumed that both the current in and are equal. This is a rough approximation because the mirrored current of is divided between and . Therefore depends on and , which results in a transcendental equation without a closed-form solution. Hence, the theoretical resistance values were used as a starting point and the final values were optimized through simulation until a minimum had been achieved. In these adjustments, it was verified that by keeping closer to the moderate inversion region (i.e.,

), the variation of over the temperature was greatly reduced than biasing in the weak inversion. Table 2 shows the final values of the transistors widths and lengths, the resistors and capacitors values. Where the capacitance is implemented by a MIM capacitor that can be placed on top of the bandgap resistors. Hence, its area was chosen to be equal to the total resistance area, resulting in a 20 pF capacitor Fig. 4 shows the simulation results of as a function of the temperature for . The maximum and minimum value of are and , respectively. Resulting in a total variation of and, a temperature coefficient (

) of 24 ppm/ºC. Fig. 5 shows the simulation results of when sweeping the supply voltage from 0 to 1.2 V at 27 ºC. The smallest voltage for the bandgap to provide a stable reference voltage is 0.6 V. Lastly, Fig. 6 shows the Power Supply Rejection Ratio performance for at 27 ºC. The highest PSRR value of -40 dB occurs for frequencies bellow 20 KHz.

Conclusion

This paper described the analysis of an all CMOS bandgap voltage reference circuit. This analysis showed that, in order to minimize the temperature variation of the reference voltage value, it is better to bias one of the diode connected transistors of the bandgap in the moderation region instead than in the weak inversion region. From this analysis a complete step-by-step design methodology was develop and described in this paper. This methodology is based on first obtaining an initial guess for the sizes of the devices and the using Spectre simulations to adjust the sizing and thus optimize the circuit in order to obtain the lowest possible of over temperature.

Fig. 1

 1 Fig. 1 Simplified schematic of the proposed Bandgap circuit.

Fig. 2 Fig. 3

 23 Fig. 2 Simplified schematic of the test bench to determine .

Fig. 4 Fig. 5

 45 Fig. 4 Simulation results of as a function of the temperature for .

Fig. 6

 6 Fig. 6 Simulation results of the Power Supply Rejection Ratio (PSRR) of the bandgap circuit for and .

Table 1 .

 1 Simulation results of of an NMOS transistor with and

	.
	1	-395.6
	5	-634.5
	10	-697.7
	20	-750.4

Table 2 .

 2 Transistors, resistors, and capacitor final sizing values.

	# Transistor
	0.64/10
	3/0.75
	3/3
	5/10
	4 /10
	# Resistor
	0.260
	2.455
	1.281
	# Capacitor
	20

Acknowledgments. The work presented in this paper was supported by Proteus project funded by the European Union's H2020 Programme under grant agreement no 644852.

 [START_REF] Doyle | A CMOS subbandgap reference circuit with 1-v power supply voltage[END_REF]Replacing ( 4) and ( 3) in ( 2) results [START_REF] Quendera | A low voltage low power temperature sensor using a 2nd order delta-sigma modulator[END_REF] Hence, the variation of over the temperature is given by [START_REF] Banba | A CMOS bandgap reference circuit with sub-1-V operation[END_REF] where . In order for to be constant its derivative must be equal to zero, [START_REF] Ytterdal | CMOS bandgap voltage reference circuit for supply voltages down to 0.6 V[END_REF] where , in which is the Boltzmann constant ( J/K) and is the elementary charge ( ). By simulating a NMOS diode connected transistor biased by a current (Fig. 2). source it possible to determine the variation with temperature (Fig. 3). The current was set to 500 nA and , the temperature was swept from -40 to 100 ºC for different values ( m, m, , and m). As expected from (1) as decreases increases. Also, decreases with the increase in the temperature, i.e. it has a negative temperature coefficient like the bipolar transistors. For the chosen current and values, and knowing that V (extracted by simulation), then must be higher than 5 m to be in the moderate region ( ) or weak inversion ( ) using even higher values. Table 1 shows the slope, i.e. , for each value from Fig. 3. Using the value of Table 1 obtained through simulation for results in [START_REF] Ytterdal | CMOS bandgap voltage reference circuit for supply voltages down to 0.6 V[END_REF].


Edgar M Silva

Ricardo Jardim-Gonçalves

Ricardo Jardim-Goncalves

Multi-criteria Analysis and Decision Methodology for the Selection of Internet-of-Things Hardware Platforms

Keywords: IoT, Smart Systems, Decision Making, Analytic Hierarchy Process, Arduino

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The emerging Internet-of-Things (IoT) technology is becoming more and more important in society. Devices are being used for environment awareness giving to people a more truly interaction with the surrounding world. With advances in microelectronic technologies, manufactures are being capable to develop small and cheap devices, to tackle different application domains and services based on IoT deployments [START_REF] Silva | Iot testbed business model[END_REF]. Hence, these devices have limited resources, for example low power, small processing and storage capabilities, and for this reason, devices must be chosen properly accordingly to the task they will fulfil. Consequently, a research question arises: "which is the more suitable device (hardware platform) to implement a certain Smart System solution in IoT?".

Multi-Criteria Decision-Making (MCDM) is one of the most widely used decision methodologies in sciences, business, government and engineering worlds. MCDM methods can help to improve the quality of decisions by making the decision-making process more explicit, rational, and efficient [START_REF] Boucher | Multi-attribute evaluation within a present framework and its relation to the analytic hierarchy process[END_REF][START_REF] Wang | Ranking irregularities when evaluating alternatives by using some electre methods[END_REF]. MCDM is an approach comprehensive of a set of techniques which provides ranking methods to classify criteria of a certain problem and assist on decision phase. MCDM can tear down complex problems into small, manageable problems, in such way that small sets of data can be analyzed from which is obtained reasonable judgements. Reassembling all pieces is then formed a coherent overall system view that will aid decision makers on their decisions [START_REF]Multi-criteria analysis: a manual[END_REF]. From this emerges the following hypothesis that could respond to the question in place: "If during a Smart System design, engineers could use a multi-criteria methodology to rank IoT hardware solutions, then more proper solutions could be selected, leading to a more suitable (i.e.: low-energy consumption, low cost, etc.) design of Smart-Systems." This work presents a methodology capable of analyse a set of hardware alternatives based on user's multicriteria requisites and advise which is the more suitable hardware solution for a specific situation. Consequently, improve in overall, a Smart System deployment and operation (see Figure 1).

Hardware Platforms of Smart Systems

People are living in an environment where Smart Systems are everywhere. These systems can be found in areas such as transportation, manufacturing, healthcare, energy, etc. In a succinct definition, Smart Systems can be seen as a system capable to sense, act and control a certain situation. Smart Systems present unique capabilities (cognitive as physical) to optimize their performance in response to external (sensed) information. With the technological advances in the microelectronic these capabilities have improved widely [START_REF] Jones | Humans' Critical Role in Smart Systems: A Smart Firefighting Example[END_REF]. Deployment of a Smart Systems can vary in many ways, in scale, functionality, implementations and technologies. Although, developers struggle with the same challenges during the deployments design phase. One of problems arises with the need to choose a suitable hardware platform (device) or platforms between a set of alternatives. Even more when manufacturers are being capable of develop new devices to tackle different application domains. These devices, also known as motes or sensor nodes, have some particular characteristics such as small size, low-power, sensing units, wireless communication and they are cheap [6]. As a result of such features they are categorized as Resource-Constrained Devices (RCD). Multi-Criteria Decision Methods (MCDM) can assist on the development of Smart Systems, in a way that a decision could be provided in terms of what will be a more proper hardware solution for a certain task. For example, sense the environment, perform a system action or apply/compute a control function.

3 Literature Review

Hardware Platforms for IoT: Resource-Constrained Devices

With the growing interest of major technology players on Internet-of-Things (IoT), it has been seen a constant evolution of microelectronic technologies, bringing to this domain a series of devices which enables the realization of the IoT concept. These devices, also known as motes or sensor nodes, have some particular features such as small size, low-power, and processing, storage, sensing and wireless communication capabilities. These characteristics, from which comes the categorization as Resource-Constrained Devices (RCD), allow Wireless Sensor Networks (WSN)s to differ from other wireless technologies. Devices are reasonably cheap, and still maintain accurateness/preciseness and reliability. Although, energy efficiency is considered to be the major requirement for these type of devices [6, 7].

Methods for Multi-Criteria Decision-Making

Multi-Criteria Decision-Making (MCDM) is a process that takes a decision upon a set of available solutions, assessing which one is more suitable across diverse, contradicting, qualitative and/or quantitative criteria. MCDM methods have been applied to engineering problems, providing useful insights to decision makers, making their decisions more qualified to overcome complex problems [START_REF] Kolios | A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs[END_REF]. Based in [START_REF] Kolios | A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs[END_REF][START_REF] Sabaei | A Review of Multi-criteria Decision Making Methods for Enhanced Maintenance Delivery[END_REF][START_REF] Aruldoss | A Survey on Multi Criteria Decision Making Methods and Its Applications[END_REF], as others could be point out, three of the most widely used decision making methods in literature are the: AHP (Analytic Hierarchy Process); PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation); and ELECTRE (Elimination Et Choix Traduisant la Realite).

Analytic Hierarchy Process (AHP). The Analytic Hierarchy Process (AHP) was proposed by Thomas L. Saaty [START_REF] Saaty | A scaling method for priorities in hierarchical structures[END_REF], and it is aimed to solve problems with multiple and conflicting criteria. AHP is a powerful decision making methodology capable of decompose a complex problem into a systematic hierarchy procedure [START_REF] Aldlaigan | A new measure of bank service quality[END_REF], as depicted in Figure 2. AHP applies a criteria pairwise comparison based on decision maker judgement, creating the Saaty 1-9 scale to define the criteria priority. In which qualifying a criterion with 1 indicates equal importance, and 9 extremely important. With an n criteria and p solutions it is achieved a decision matrix of p*n size, in which the {dpn} element indicates the value for pth solution in respect to the nth criteria. The pairwise comparison matrix is a m*n size, in which the {vmn} element indicates the value for mth criteria in respect to the nth criteria. Normalized comparison matrix has its elements calculated as follows: Nvpn=vpn/wn ; wn=∑vpn, p=1, 2 .... The contribution of each criteria n for the problem objective, i.e. the eigenvector, is then computed as: CWn=∑wn/n, n=1,2 ... [START_REF] Figueira | Multiple Criteria Decision Analysis: State of the Art Surveys[END_REF]. Identified the eigenvector, is then possible to rank the solutions through a simple matrix multiplication, i.e.: the decision matrix multiplied by the eigenvector.

PROMOTHEE. Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) [START_REF] Brans | A preference ranking organization method: The PROMETHEE method for multiple criteria decision-making[END_REF] is a MCDM method, with six different versions based on ranking. The first version, PROMETHEE I, consists on partial ranking; PROMETHEE II on complete ranking; PROMETHEE III performs ranking based on intervals and PROMETHEE IV is a method for the continuous case; PROMETHEE V method has integer linear programming and net flows; finally the PROMETHEE VI method includes a representation of the human brain [START_REF] Figueira | Multiple Criteria Decision Analysis: State of the Art Surveys[END_REF][START_REF] Mela | Comparative study of multiple criteria decision making methods for building design[END_REF]. The PROMETHEE methodology can be described in five steps, as Figure 3 shows. The decision maker chooses a preference function defined into two independently actions (e.g. Boolean function -value 0 or 1), and then it is applied a solutions comparison. The comparison results and criteria values are used to form a matrix, on which methods can then be applied. For example, PROMETHEE I to create a partial solutions rank and PROMETHEE II to obtain a final solutions ranking [START_REF] Mateo | Multi-Criteria Analysis in the Renewable Energy Industry[END_REF]. PROMETHEE methods I and IV use preference functions which are more suitable for qualitative criteria. Methods type III and V are normally chosen for quantitative criteria problems. Type II and VI preference functions are used with less frequency. ELECTRE. Elimination Et Choix Traduisant la Realite (ELECTRE -Elimination and Choice Expressing the Reality) method was created by Bernard Roy [START_REF] Roy | The outranking approach and the foundations of electre methods in Theory and Decision[END_REF]. Similar to the previous decision method, ELECTRE has also six variations: type I, II, III, IV, IS and TRI. All ELECTRE methods are based on the same background, but have different process. In ELECTRE I and IS a single solution or a group of solutions are selected and assigned as a possible solution. ELECTRE II was developed to deal with problems that need to rank solutions from the best to the worst solution. ELECTRE III introduced pseudo-criteria and fuzzy binary outranking relations. ELECTRE IV method was possible to rank solutions without the use of relative criteria coefficients (the only ELECTRE method that did not use it). Finally, ELECTRE TRI assigns categories to solutions [START_REF] Kolios | A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs[END_REF][START_REF] Brans | A preference ranking organization method: The PROMETHEE method for multiple criteria decision-making[END_REF]. ELECTRE is a preference-based model, performing a pair-wise comparison between solutions. The model assumes three types of relations between solutions: is preferred to; is indifferent to; and incomparable to. ELECTRE brings the notion of thresholds values, used to set concordance and discordance indexes. These indexes are used to classify a solution in relation to another. Summary. The Analytic Hierarchy Process (AHP) follows a prioritization theory, dealing with complex problems that need multi criteria consideration simultaneously. Although, AHP presents some disadvantages. The pairwise comparison based on decision makers' subjective judgment, and the criterion weight has a direct impact on the final score. The PROMETHEE method is consistent, easy to use and does not need great interaction with decision makers. Although, PROMETHEE presents as downside the incapability to react when new alternatives are introduced. The method II is unable to present a preferred solution in a bi-criteria problem, after the method I have been applied. Generally, ELECTRE method does not frequently leads to the case in which one solution stands out from the others. For this reason the method is considered to be more suitable for problems with several solutions and not so many criteria. Also, it is considered to be difficult to understand, and thresholds definition is a problem since there is no "correct" value.

Proposed Methodology

This section will describe the proposed methodology which enables users, developers and researchers to assess which hardware platform is more suitable for their own application. The methodology provides means to indicate restrictions if any and/or criteria optimization methods, combined with a criteria prioritization based on decision maker judgement.

Possible solutions, i.e. hardware platforms, are represented by set S in ( 1):

(1)

The amount of alternatives is a finite number and is defined by the user, i.e. users select the solutions that will be analyzed. Platforms have a vast number of

Case Study on Hardware Platforms

The selected hardware platforms chosen to validate the proposed methodology are based on Arduino platform. Five different devices were selected, represented by set S, as described in [START_REF] Aruldoss | A Survey on Multi Criteria Decision Making Methods and Its Applications[END_REF]. Arduino devices present several features and characteristics suitable for the development of sensor networks. Among the vast number of features, were selected eight to serve as assessment criteria, as described in [START_REF] Saaty | A scaling method for priorities in hierarchical structures[END_REF]. Table 1 summaries the selected hardware platforms and values for each feature. Possible solutions and features are placed in the same order as given in [START_REF] Aruldoss | A Survey on Multi Criteria Decision Making Methods and Its Applications[END_REF][START_REF] Saaty | A scaling method for priorities in hierarchical structures[END_REF]. [START_REF] Sabaei | A Review of Multi-criteria Decision Making Methods for Enhanced Maintenance Delivery[END_REF] . ( 10) The values for features Cost, Clock speed, Data Ram Memory, Program Flash Memory and Weight, respectively , were retrieved from Arduino page 1 . Values for features Used Memory Ram and Program Flash when an Empty application is installed on the selected Arduino devices, respectively , were retrieved using Arduino IDE version 1.5.7. Arduino IDE was used to program the Empty application into devices. Empty application is the Arduino operating system without any kind of operation, mainly the operating system footprint. Values of feature Energy , were obtained by placing a multimeter between the power source and the Arduino and then was read the current consumed (milliamps). The Evaluation Table ,  , can be obtained directly from Table 1.

To define the Analytic Hierarchy Process (AHP) table, , reflecting user criteria importance/preference, was performed a research using popular search tools (IEEE eXplore, Harzing's Publish and BASE). The research was based on titles that contain some key words. The key words "Internet" and "Things" were stuck and varying worlds such "energy", "cost", "weight", etc. The results are summarized in Table 2. The AHP table ,  , presented in ( 12) is formed basically from the preformed research. Notice that this AHP table will be used in the application scenario in Section 6. It also reflects a preference example, since criteria importance can differ widely from one user to others, and it also depends on application purposes. From , is possible to compute the normalized AHP comparison matrix, , from which the eigenvector, i.e. importance of each feature on selecting the more suitable solution, is obtain. The eigenvector is presented in [START_REF] Figueira | Multiple Criteria Decision Analysis: State of the Art Surveys[END_REF]. [START_REF] Aldlaigan | A new measure of bank service quality[END_REF] 6 Application Example: Small Storage Unit For application example was selected one scenario, a Small Storage Unit, where it is needed a hardware platform which presents a small energy consumption, it is cheap and has a large amount of memory available. Since there is no available data regarding energy consumption for the specific application, let's consider the energy consumption of the operating system footprint (in this case Arduino operating system). Accordingly, is then possible to build the Assessment Constraints set, , presented in [START_REF] Brans | A preference ranking organization method: The PROMETHEE method for multiple criteria decision-making[END_REF], and the Score Table ,  , as described in ( 15):

The , presented in ( 14) minimizes the first criteria (energy consumption), , to classify the available hardware platforms in energy consumption terms. Constraint, , to minimize the cost, and constraints , to maximize the available memory. ( 14)

Results Comparison. Table 3 presents the final classification results for each available hardware solution. Three different evaluations are available to perform a methods comparison. The first column shows the results when AHP method, alone, is applied to features values. As discussed in Section 3.2, this method presents a direct influence of feature values on the final score. Also, it is not able to reflect user assessment constraints (e.g. min, max). Considering, the objective to minimize all selected criteria, this method advice Uno Rev3. But the scenario also includes memory maximization, leading to DUE solution. Normally, using AHP method the solution with the higher score is considered the best one. In this scenario that is wrong, excessive energy consumption and cost. Consequently, the result is inconclusive for the presented case study. Since the application objective is more than that, the midway result, , which consists only on assessment constraints enforcement, indicates that the proper solution is Mini 05. The complete proposed methodology execution indicates the same solution, Mini 05, but reflects criteria preference and removes all solutions draws. This methodology brings to developers the capability to select the proper solution for a certain task from a set of available solutions. Provides methods to select criteria importance and it is also able to apply constraints functions to features, improving solutions assessment.

Conclusion and Future Work

There are many features/criteria which can influence the overall performance of an application running in a hardware platform. Also, developers have different perspectives of which feature is more important. The proposed methodology enables users, developers and researchers to assess which hardware platform is more suitable for their own application, depending on the application scenario. It is possible to indicate restrictions (e.g.: must have a certain feature, thresholds for feature values), and apply criteria optimization (minimize or maximize). The solutions ranking is attained using a well-known decision making method, Analytic Hierarchy Process (AHP), aimed to solve problems with multiple and conflicting criteria. Methodology results were able to point out the better/proper solution from the available ones. It also presents a more clear solution ranking compared to the AHP method, which presents an ambiguous result. Future work involves the extension of the analysis to different hardware elements/features. A further step will consider the inclusion of hardware platforms formal descriptions, to facilitate the inclusion process of new criteria.

Fig. 1 .

 1 Fig. 1. Multi-Criteria Decision Methodology for the Selection of IoT Hardware Platforms.

Fig. 2 .

 2 Fig. 2. Example of a criteria and solutions hierarchy.

Fig. 3 .

 3 Fig. 3. PROMETHEE methodology.

Table 1 .

 1 Hardware Solutions versus Assessment Criteria.

	Solutions:	Features/Criteria:
		25.5	20	16	2	32	9	450	25
		34.2	35	16	8	256 9	642	37
		113	36	84	96	512 0	10492 36
		31.5	14	16	2	32	9	450	6
		41	18	16	2.5	32	157 4264 13

Table 2 .

 2 Research Results Summary: A criteria preference/importance.

	Energy	Cost	Processor Memory	Weight
	Percentage [%] 83.9	9.6	3.5	3	0
					(11)

Table 3 .

 3 Application Example Results.

	AHP		FSR
	61.38	17	2.93
	107.81	17	2.40
	1124.9	16	1.77
	61.96	18	2.96
	448.97	16	2.24

https://www.arduino.cc/

Acknowledgments. This research work was partially supported by funds provided by the European Commission in the scope of FoF/H2020-636909 C2NET.

features,

, although user may or may not consider all of them as important. The set of features/criteria, called F, is defined as in [START_REF] Boucher | Multi-attribute evaluation within a present framework and its relation to the analytic hierarchy process[END_REF].

(2)

With the sets of possible solutions and criteria defined is then possible to create an Evaluation Table . The Evaluation Table ,  , given by [START_REF] Wang | Ranking irregularities when evaluating alternatives by using some electre methods[END_REF], is an n-by-m matrix. Rows represent the contemplated solutions and the columns the assessment features. The element represents the value for feature j of the alternative i.

(

Hardware platforms, in our case the possible alternatives, present a vast number of features, and each feature has its own units. Features values are treated as dimensionless. Hardware platforms, in our case the possible alternatives, present a vast number of features, and each feature has its own units. Features values are treated as dimensionless. To perform an analysis on a set of hardware alternatives, considering user's multi-criteria requisites, to obtain an advise on which is the more suitable hardware solution for a specific situation, users must indicate restrictions if any and/or criteria optimization method (minimize or maximize). Currently the proposed methodology presents seven types of restrictions/optimization, from now on call Assessment Constraints, which can be applied by users to assess the more suitable solution(s). The Assessment Constraints set, AC, is defined in [START_REF]Multi-criteria analysis: a manual[END_REF]. Each constraint can be applied to one or more criteria. Two or more constraints can be applied to single feature/criteria.

(3) Assessment Constraint min and max (optimization methods) are used respectively when user's purpose is to minimize or maximize values of a feature. Constraint mustHave is used for cases in which a feature must be present. Constraint cannotHave is used in the opposite case, i.e. criteria/feature cannot be available in the final proposed solution(s). Constraints lessThan, equal and greaterThan, enables users to set threshold values or look for a specific feature value. When users apply the Assessment Constraints, points are assigned. In the case of min and max, points are assigned from 1 to n points. For instance, in a minimization applied to a feature , a solution with the smallest value for get n points, and the solution with the higher value in get 1 point. The same principle is used for maximization, 1 point for the lowest value and n points for the higher. For the other Assessment Constraints, it is assigned 0 or n points. For example, the mustHave case, a solution with value different from zero gets n points, if the value is zero (which indicates that the feature is not present in that solution) it get 0 points. When more than one Assessment Criteria is applied to a single feature, assigned points are multiplied. Applying the selected Assessment Criteria to the Evaluation Table ,  , is formed the Score Table , , formalized next in ( 5): [START_REF]Multi-criteria analysis: a manual[END_REF] To this point, users are able to optimize features using minimization or maximization methods. Are able to set binding rules between solutions and its features, as well as define thresholds for feature values. However, it is not yet possible to assign preference between features, i.e. features priority. To achieve this goal, the proposed methodology takes use of Analytic Hierarchy Process (AHP) presented in Section 3.2. AHP applies to features a pairwise comparison based on decision maker (users) judgement, using the Saaty 1-9 scale to establish criteria priority. Qualifying a feature with 1 indicates equal importance, and 9 extremely important. The AHP comparison matrix, , is given in (6). The element represents the comparison value for feature j and feature k. It is also important to note that the comparison value, , has the inverse value of . Specifically, the impact of feature j in feature k has the inverse importance of feature k in feature j. The AHP comparison matrix, , is then normalized as presented in [START_REF] Moschitta | Power consumption assessment in wireless sensor networks[END_REF]. The contribution of each feature/criteria on the selection of a proper solution is given by the eigenvector and it is computed as described in [START_REF] Kolios | A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs[END_REF]. The final solution ranking, , is then given by ( 9).

(


Javaneh Ramezani
email: m.ramezani@fct.unl.pt

Javad Jassbi
email: j.jassbi@uninova.pt

A Hybrid Expert Decision Support System Based on Artificial Neural Networks in Process Control of Plaster Production -An Industry 4.0 Perspective

Keywords:

Emerging technologies could affect future of factories and smartness is the main trend to receive that points. Quality was important and will be crucial in future but the question is how to build Smart Systems to guaranty quality in workshop level. This is an important challenge in Industry 4.0 paradigm. In this paper the main objective is to present practical solution under the light of Industry 4.0. The aim of this study is to presents propose a Hybrid Expert Decision Support System (EDSS) model, which integrates Neural Network (NN) and Expert System (ES) to detect unnatural CCPs and to estimate the corresponding parameters and starting point of the detected CCP. For this purpose, Learning Vector Quantization (LVQ) and Multi-Layer Perceptron (MLP) networks architecture have been designed to identify unnatural CCPs. Moreover, a rule based ES has been developed for diagnosing causes of process variations and subsequently recommending corrective action. The proposed model was successfully implemented in Construction Plaster producing company to demonstrate the capabilities and applicability of the model.

Introduction

Minute variations or/and differences always exist in any production process, regardless of the quality of design and maintenance. SPC is a powerful and capable set of solving problem tools, which is very useful in stabilizing the process and improving its efficiency by reducing variability [START_REF]Statistical Quality Control Handbook[END_REF]. One of the main objectives of SPC is quickly realizing the existence of assignable causes and any change in the process to investigate the reason of the diversions and applying corrective actions to avoid more defective products in lines. The most essential process of implementing SPC is control charting. Control charts are useful in determining the process behaviour [START_REF] Montgomery | Introduction to Statistical Quality Control[END_REF]. Usually existence of the non-randomness trend in the control chart has significant impact on process performance. In the meantime, control chart patterns such as Cycle, Trend, Shift, and Systematic as the basic patterns, have the process roots and usually appear in the most control charts of qualitative characteristics. As control charts consider only the current sample data, are not capable of presenting any pattern-related information. On the other hand, applying the accessible run rules can lead to increase false alarms [START_REF]Statistical Quality Control Handbook[END_REF]. Since the analysis of control charts is difficult, because it requires statistical knowledge and also experience of process, the main motivation of this study is to deal with the challenge of developing intelligent system which could identify defects, detect resources of deviations and recommend corrective actions automatically. Our investigation shows there is no existing model capable of handling mentioned challenge. To address this specific challenge, the main research question would be:

What would be a suitable intelligent methodology to describe and analyze control charts for the fault diagnosis of the process in order to advise decision makers?

Considering the fact that neural networks are well used alternatives for pattern recognition, and expert systems are effective in identifying causes of deviations and also recommending corrective action, we will try to answer the research question merging mentioned models using current successful experiences in both areas. The rest of paper is organized as follows: section 2 is devoted to the literature review. Research methodology is discussed in section 3. In section 4 EDSS model is presented and in section 5 conclusions are provided.

Contribution to Smart Systems

Smartness is the heart of Industry 4.0. Smart systems refer to diverse range of technological systems that can perform autonomously or in collaboration with other systems. These systems are capable to combine functionalities including sensing and controlling a particular situation in order to describe and analyze it. Smart systems have the ability to predict, decide and communicate with the user through a user interfaces. Smart systems have been also used in different areas, such as energy, transportation, security, ICT, industrial manufacturing, control, etc. [START_REF]European Commission: Smart components and smart systems integration[END_REF]. The intelligence of smart systems is associated whit autonomous operation based on adaptability and learn-ability. This idea carries a sense of evolution and refers to process of modification and improvement over time. Neural networks with the ability of learning and expert systems with the command and rule-based features are perfect examples of smart systems [START_REF] Moshnyaga | Guidelines for developers of smart systems[END_REF]. In control process area, the main purpose of utilizing smart systems is to develop an intelligent system for real time control and monitoring the process. This work intends to contribute with the issues related to supervisory aspects of smart systems, considering the data acquisition, information transmitting, command-and-control and cognitive features of NNs and ESs. The main objective is to provide a model to support more intelligent and adaptive monitoring of smart system.

State of the Art

In this section related works that have used ESs and NNs to interpret SPC charts, were reviewed.

Application of ESs in SPC

In order to be competitive in the global market, more attention should be paid to extracting quality engineering knowledge in a systematic manner. Availability, consistency, extensibility and testability are the major advantages of ESs for SPC users [START_REF] Feigenbaum | The rise of the expert company: How visionary companies are using artificial intelligence to achieve higher productivity and profits[END_REF]. In the following application of ES in SPC shall be briefly described. Evans [START_REF] Evans | A framework for expert system development in statistical quality control[END_REF] designed an ES for interpretation of x-bar and R charts using three sets of rules. [START_REF] Pham | control chart pattern recognition using multi-layer perceptrons and learning vector quantization networks[END_REF] proposed a knowledge-based SPC system based on general knowledge of the process, which is capable of monitoring variations in a process. In another study [START_REF] Pham | control chart pattern recognition using learning vector quantization networks[END_REF] they proposed a hybrid system for SPC implementation. This system used ANN models to analyze control charts and an ES to diagnose the plausible causes. Their system can only recognize patterns that have full features. Reference [START_REF] Masud | A Knowledge-based advisory system for statistical quality control[END_REF] developed a knowledge based assistance for monitoring the process. Walter [START_REF] Chung | An ANN-based DSS system for quality assurance in production network[END_REF] integrated a model of decision support system (DSS), ANN, SPC and ICT to facilitate making decisions in production line. Reference [START_REF] Lyu | Automated visual inspection expert system for multivariate statistical process control chart[END_REF] employed image processing and multivariate SPC to develop a visual detection ES. [START_REF] Chang | Expert system for multi-criteria decision making to assess the collaboration commerce systems[END_REF] has focused on ES and SPC, for selecting collaborative commerce system. And [START_REF] Garcia-Escudero | Robust multivariate control charts for early detection of broken rotor bars in an induction motors fed by a voltage source inverte[END_REF] developed an ES, based on multivariate control charts, to fault detection in induction motors.

Application of ANNs in SPC

The principle reason for applying NNs in SPC is to automate SPC chart interpretation [START_REF] Jacob | Training Artificial Neural Networks for Statistical process control[END_REF]. This author has divided the literature of this area into structural change identification (changes in the average or variance of process) and pattern recognition. Most of the early research focused on detecting mean and variance shifts using similar approaches to [START_REF] Pugh | Synthetic Neural Networks for Process Control[END_REF] including [16, 17 and 18]. Cheng [START_REF] Cheng | A Multi-Layer Network Model for Detecting Changes in The Process[END_REF][START_REF] Cheng | A Neural Network Model Approach for The Analysis of Control Chart Patterns[END_REF] designed multi-layer networks to simulate variance change. [START_REF] Niaki | Designing a multivariate-multistage quality control system using artificial neural networks[END_REF] presents a NN-based approach for detecting bivariate process variance shifts. NNs in the issues of pattern recognition are used to determinate random and non-random patterns. For example, [START_REF] Cheng | Using neural networks to detect the bivariate process variance shifts pattern[END_REF] used LVQ to detect normal patterns, trends, sudden shifts and cycles. [START_REF] Guh | Neural network based model for abnormal pattern recognition of control charts[END_REF] proposed a hybridlearning model using back propagation networks (BPN) and decision trees. [START_REF] Guh | Optimizing feed forward neural networks for control chart pattern recognition through genetic algorithms[END_REF] proposed a selective NN ensemble approach for CCP identifications. [START_REF] Yu | A Neural Network Ensemble Approach for Recognition of SPC Patterns[END_REF] proposed a NN to address the problem of monitoring a multivariate-multistage process. [START_REF] Niaki | Designing a multivariate-multistage quality control system using artificial neural networks[END_REF] using the method of Fourier descriptors and NNs identifies the CCPs. In the [START_REF] Phokharatkul | Control chart pattern classification using fourier descriptors and neural networks[END_REF] the hybrid model of Recurrent Neural Network (RNN) and regression were utilized to recognize the CCPs. [START_REF] Behmanesh | Control chart forecasting: A hybrid model using recurrent neural network, design of experiments and regression[END_REF] developed a NN classifier for CCPs by Generalized Autoregressive Conditional Heteroskedasticity (GARH) Model. [START_REF] Lavangnananda | Neural network classifier of time series: A case study of symbolic representation preprocessing for Control Chart Patterns[END_REF] Applies multivariate exponentially weighted moving average (MEWMA) and NNs for identifying the start point of the variations. [START_REF] Ebrahimzadeh | Recognition of control chart patterns using an intelligent technique[END_REF] presents combination of intelligent model of ANN and support vector machine (SVM) learning methods for CCP recognition. [START_REF] Thaiupathump | lmpact of kurtosis onperformance of mixture control chart patterns recognition using independent component Analysis and neural networks[END_REF] is an attempt to separate basic and mixture CCPs using NN and Independent Component Analysis (ICA). [START_REF] Misseno Da Cruz | Application of Paraconsistent Artificial Neural Network in StatisticalProcess Control acting on voltage level monitoring in Electrical Power System[END_REF] is an example of using Paraconsistent ANN (PANnet) and SPC in electrical power System.

Research Methodology

As the conceptual model is observed in the Fig. 1, in this study the structure of EDSS model based on ANNs has been presented to control the plaster manufacturing process. In order to describe the implementation steps of the model and validate it, the case study has been done in Semnan Noor plaster factory, which is producer of construction plaster and micronized plaster, according to Iran National Standard No. 1-12015 in the field of construction plaster production. The investigated product in this study is POP (Plaster of Paris) or construction plaster that is produced through calcinations process at the temperature of 150℃ in a closed reactor system called baking kiln while gypsum loses 1.5 mole of molecule water and converts to the construction plaster. In this study, order to improve the quality of the process, after identifying all parameters of each control station in the plaster production process, the interview was done with the experts focusing on the importance of each variable in the process. Then, according to the existing records, the causes of defect occurrence in the construction plaster production that were associated with quality features of the plaster were examined. Finally, initial setting time of the plaster was diagnosed as the critical parameter of the process that should take at least 7 minutes and at most 15 minutes. This parameter is dependent on the plaster's crystal of water after baking while the acceptance range in the investigated factory is between LSL = 5.0 to USL = 5.08 weight percentage and the process works under control with the basic limits of about LCL=5.26 to UCL=5.56. This parameter as a CW index has been analyzed by "Failure Modes and Effects Analysis" (FMEA) and "SPC" methodologies. The statistical population of this study included baked plaster of Low Burn kiln in the plaster production process that has been produced in a specified time period by stratified random sampling method from the baking tail silo, at random times, different shifts and operators. According to previous research on quality control issues, 25 subgroups of 5 samples (n =125) were found suitable. Due to the nature of plaster production process, 8 times per shift and each time, five 25 grams' sample from a random point of the silo was taken to measure.

Model Design Construction

EDSS is composed of 3 subsystems. The first subsystem using statistical formulas, determines control chart limits for sampled data (weight percentage of CW), calculates process capability (Cpk) and gives warning if any of the points is out of control limits. The second subsystem is responsible for identifying unnatural patterns and the purpose of third subsystem is interpreting the causes of deviations and recommending preventative or corrective action.

Developing an ANN

In this section first the procedure of pattern simulation and then the development steps of NN will be explained.

Simulating Different Patterns of the Process Control Charts. In statistical issues any natural deviations could be determined according to probability distribution function of the corresponding random variable. This is the base of natural behaviors simulation in different charts of process control [START_REF] Fatemi Ghomi | Design a model based on Neural Networks to pattern identification[END_REF]. In this study, due to lack of large volume of data for unnatural patterns, it is tried to simulate the data, according to the original data of process. Overall structure of NN model is composed of, two separated sets, module I and module II (Fig. 2).

Input: Weight percentage of CW parameter in Sampled of POP             ) ,...X X , Min(X - ) ,...X X , Max(X = X : chart R For 5 ) ,...X X , (X X : chart Xbar For 5 2 1 5 2 1 i 25 2 1 i 25 2 1 I X ,..., X , X X Expert Decision Support System (EDSS) Output:      1 CpK IF capable) is (Process 1 1 CpK IF ) inacapable is (Process 0 1 Y             3 CL 3 - CL IF control) under is (Process 1 3σ CL X OR 3σ CL X IF control) of out is (Process 0 X Y 2            ,.
Topology of the LVQ Network Designed in Module I. First part of the model has been designed with general aim of identifying and classifying the input patterns. For this purpose, using competitive algorithms instruction, a LVQ network with two half-connected layers has been designed. The recognition window has 25 components (input layer neurons). In the first layer of network, 175 neurons and in the second layer 8 neurons are considered. The main reason in determining the required number of first layer neurons, were significantly identification incorrectly patterns. In addition, it has been tried to assign almost equal neurons to patterns with equal number of parameters (Table 1). 2), and also estimating the starting point of the unnatural patterns. For this purpose, 7 two-layer perceptron networks, analyze the basis and simultaneous patterns. Because different directions of changes in process (upward or downward pattern) should be well recognized, so due to considering the output values in the range [-1, 1] the activation function has been considered bipolar sigmoid with a constant A = 0.1. Total input of all MLP networks in module II is 26 that one of them is for bias and is considered equivalent to one. In all MLP networks for a special error value of the network, the number of required iterations to reach the desired error is calculated and the lowest repeated number until reaching to the considered error, is selected as the number of hidden layer neurons (Table 3). In output layer of each MLP, the number of neuron is selected according to the number of corresponding parameters of each pattern.

Fig. 2. Structure of NN Mode

NNs Training. The LVQ network in module I with learning rate =0.01 is trained by enabling competition to take a place among the Kohonen neurons. The competition is based on the Euclidean distances between the weight vectors of these neurons and the input vector. The training method used in MLP networks is back propagation with adaptive learning rate, where the weight of each layer, by the output and output derivative is corrected until the network is fully trained [START_REF] Fatemi Ghomi | Design a model based on Neural Networks to pattern identification[END_REF]. The training data set is applied to corresponding networks in a category form and error is calculated at each step until the learning process performed. Network's error which is a cumulative error is defined as below in which: p stands for pattern number, o output neurons and desired value for the j output of i pattern.

.

(

) 1
In this study, there were total of 11,000 training samples. 4000 samples as training data set in the LVQ network which for each pattern equally 500 data have been assigned and 7000 samples in the MLPs which for each of 7 networks equally 1000 samples were produced. In order to producing training set, first maximum and minimum values of input parameters were defined and then desired data were scaled and mapped considering the type of used transfer functions.

.

(

) 2
The scaling data range in LVQ network between [-5, +5] and in MLP networks [-1, +1] is considered. But before that the inputs of MLPs, between [3.4, 7.4] had been scaled and the outputs with separate max and min values scaled (Table 4). Because each pattern has two orders of changes (upward and downward), the desired output is set in form 1 or -1. For example, the output vectors of Natural would be 3. Test the NN Model. In the training stage, the network's efficiency increases by minimizing the error between real outputs and in the testing step only the input data are given to the network, which are validated through the response prediction, between input and output variables.

Evaluation of LVQ Network in Module I. One of the major criteria in the development of LVQ network in purposed model has been the reduction of possibility of incorrect identification. Considering the diversity of trained patterns, the model shows a good response in this problem and also in indirect detection of individual patterns and incomplete diagnosis of mixed behaviors (Table 5). Moreover, for each identification window (input vector) network makes a decision on the process status. Hence the occurrence possibility of errors related to the decision making topics will be discussed. If the network detects by mistake the behavior of the under control process as unnatural, type I error and if the unnatural pattern in the process could not be detect, type II error has taken placed. Performance of module I and LVQ network by 400 test vectors were measured. Each of 400 test vectors is a sample of 25 Pieces that represents one of the 8 identified patterns. Amount of LVQ error is calculated 0.052 and the results of 400 experimental vectors is integrated in Table 5.

Test and Evaluation the MLP Networks in Module II. One of the critical problems in NNs training is the over-adaption problem of training data. Topics "generalization" and "training" in NNs have the same significance [START_REF] Menhaj | Computatianal Intelligence[END_REF]. For solving this problem, part of the training vectors is considered as validation data. In this way training data follow the flow of parameters modification and validity data will follow error during the learning process. The validity data set error such as training data set error naturally decreases but as soon as there is over learning in the network, despite fixing or decreasing error of validity data set, the amount of training data error will increase. In this time the training process stops and the parameters according to the minimum errors of validity data, will be considered as an algorithm ultimate answer. After the training process, performance of the network was tested by several examples. Results show, module II in identification and analysis of defined parameters, acts successfully and efficiently (Table 3).

Verification of the NN Model. After evaluating the NN model by the test data set, in order to determine the accuracy, repeatability and the amount of results stability in the test repetition, the verification of the model by comparing the amount of NN error and discriminant analysis (DA) error has been done. It should be noted that DA is a similar statistical method in classification and in this study the statistical software SAS was used for this purpose. Fig. 3 shows related errors to any classes in the Rate row, and in the Priors row the weight of each class is shown. 0.3325 represents the total error in using DA for the test data set. As it is seen in Fig. 4, neural networks compared to DA method, has much better performance and precision. and dialogue to the user. EDSS which can be called intelligent statistical process control (ISPC), its components are as follows:

(1) Knowledge base, which is composed of three main parts below: (a) Events, which extracts from the records of ISO, preventive maintenance, calibration, brainstorming sessions and FMEA forms. (b) Procedures, which Includes technical instructions, plaster production standard, ISPC manual and etc. (c) Rules, which consists of 60 rules extracting from experts and documents such as Western Electric tests, statistical formulas and NN's analysis, has presented in the form of IF-THEN statement.

Western Electric tests which are using general knowledge of process to control charts interpretation is including: points out of control limits (1 point more than +3σ or -3σ), gradual changes in levels (9 points larger or smaller than CL), trends (6 points in a row steadily increasing or decreasing), Systematic variations (14 points in a row alternating up and down), cycles (4 of 5 points more than +2σ or -2σ), and mixtures (8 points in a row more than +1σ or -1σ). IF interpretation is "Upward Trend" AND Failure Mode is "Increase of Crystal Water" AND Process Index is "Decrease of kiln's temperature" THEN Special Cause can be "Fuel nozzle Chocking" AND Corrective Actions can be either "Cleaning the fuel nozzle, establishment of PM for the burner or installing fuel filter".

Increase of crystal water

(2) Inference Engine, which backward-chain inference engine has used in this study in order to troubleshooting. This means, the written program has performed with two sets of rules. The first group define goals for the properties and if it is unable to determine the value of the properties with the existing rules, the user is asked to identify them. The second group updates actions like modifying the rules and transmitting the satisfied goals.

(3) Working Memory, which Consists of events and facts to be applied by the rules. This memory is used to store temporary data that is provided during problemsolving process. This data includes the user's answers to the system's questions and also deriving facts from reasoning process like unnatural patterns, the starting point and corresponding parameters of patterns that are identified by the NN.

(4) User Interface, which works with the inference engine and the knowledge base in order to two-way communication between user and the EDSS. Users can answer a question by selecting Yes or No or select one item from a menu on the screen.

Integration of NN and ES

To develop ISPC, capabilities of NNs and ES are used in automatic interpretation of control charts. The purposed system is able to do most of traditional SPC operations including calculation of control limits (LCL, CL and UCL), calculation of Cpk, data normalization test and checking for X-bar and R charts being under control. In SPC first the based charts must be prepared. For this purpose, after initial sampling, control limits and Cpk must be calculated in control mode. In this study according to the plaster factory experts' opinion if "Cpk >1" the chart is accepted as the basis (Fig. 6).

Experimental Result of EDSS Model. Using real data from the current process of plaster producing and interface software, a practical example is presented (Fig. 7). As shown in figure 6 although there is no any out of control mode in the R-chart but the process is not capable of meeting specifications because the "Cpk <1" and equal to 0.81. On the other hand, by selecting x-bar chart, the user has faced with the message of "X-bar chart is out of control". Then, the ES using Western Electric tests has announced that points falling outside the control limits may be the result of: carelessness in the measurement, machinery stop or off spec materials. Later, ES suggests the user to check the unnatural patterns by NN. As you can see, the NN has not only has identified downward shift pattern in X-bar chart but it has also estimated the starting point of unnatural pattern (point 6) and the displacement parameter (-0.161). After that, ES asks the user to explain the defect to find causes of potential failure modes in the process. If the answer is not in the options, ES says: "refer to the experts!". In this example according to the appearance of downward shift pattern and as the user observation was "kiln body scarlet", the cause of defect has announced "temperature exchange of kiln with environment due to the loss refractory and thickness" and "establishment of maintenance and inspection of refractory" have proposed as corrective or preventive actions. In this practical example, after doing corrective action and re-sampling the process, the control charts did not show any out of control mode and also process capability improved from Cpk=0.81 to Cpk=1.15. Ultimately, the designed system examined by several examples and the results were considered satisfactory. However, the system performance needs to be improved more so it is important that the long-term use of the model be identified as part of the objectives of the study because the model design is highly influenced by it.

Fig. 1 .

 1 Fig. 1. Conceptual Model

  Cycles (Cyc.) x(t) = n(t)+ l *sin( l =[ [0.1,0.3] T =8,12, … T is considered variable Systematic (Sys.) x(t)= n(t)+ g *cos( )=n(t)+u.b+l*sin( )=n(t)+s.t+l*sin( s =[ [0.01, 0.03] Topology of MLP Networks Designed in Module II: The second part of the model has been developed with the aim of estimating the parameters of each unnatural CCP, based on determined definitions (Table

  [10000000] and Downward Shift [0-1000000]. Maximum cumulative error (MCE) for training the LVQ is calculated 0.047(188 in 4000 training data), and for testing 0.0525. MCE and training iteration of each MLP can be seen in Table

Fig. 3 .Fig. 4 .

 34 Fig. 3. DA error in the test data set classification

5 . 6 .

 56 Cause and effect diagram for increasing of Crystal WaterTable FMEA form for the critical parameter (Crystal Water) Example. The Following example represents a kind of typical rule, based on specific knowledge of process:

Fig. 6 .

 6 Fig. 6. SPC operations

Fig. 7 .

 7 Fig. 7. Results of EDSS for a practical example

Table Y7 :

 Y7 Corrective or preventative action's Table

	3 Y	:	Patterns	00010000 ,... Systematic , 00100000 Cycle, , 01000000 Trend, Shift, , 10000000 Natural,	..
	4 Y	:	Parameters	 Shift,     b	  Tremd,... 3σ 1σ	3σ		1σ	   s ,	0.1σ	0.3σ	 	0.3σ		0.1σ	 ...
			:	Starting	Point	   0 0		X		12			Otherwise Pattern IF	is	Natural

5 Y

Y6: Causes of deviation's

Designing the Structure of NN Model.

Table 1 .

 1 Neurons of the first layer

	Class	Subgroups	Class	Subgroups
	1.Natural	1	5.Systematic	4
	2.Upward Shift	12	6.U.Shift+U.Tre	18
		nd
	2.Downward	12	6.D.Shift+D.Tre	18
	Shift	nd
	3.Upward	18	7.Shift+Sycles	27
	Trend
	3.Downward	18	8.Trend+Sycles	27
	Trend
	4.Cycles	20	Total

Table 2 .

 2 Range of changes in unnatural patterns parameters

	Pattern type X(t)	Range	of	parameters	Descriptions
		changes
	Shift(Sh.

Table 3 .

 3 Optimum number of hidden layer neurons, iteration trainings and maximum cumulative error in module II.

	Network name	Maximu	Number	Minimum	Neuron of	Output	Error of
		m	of	number in	hidden	numbe	MLPs
		cumulati	training	each	layer	r
		ve error	repeat	training
	1.Natural	10	10	10537	14	2	0.01
	2.Upward Shift	18	13	16423	15	2	0.018
	2.Downward	25	10	35360	22	2	0.016
	Shift
	3.Upward Trend 12	10	48343	17	2	0.012
	3.Downward	18	10	48343	25	3	0.012
	Trend
	4.Cycles	25	11	46703	23	4	0.012

Developing an ES

Steps discussed in this section include knowledge acquisition, knowledge representation and implementation: Knowledge Acquisition. In this study following the knowledge management system (KMS) approach, to help the diagnostic process, Western Electric tests are used as general knowledge and in order to obtain specific knowledge of the process, first Cause and Effect diagrams were prepared to determine the most problem causes in the process (Fig. 5). After several reviews, effective variables on rate of crystal water were categorized in two categories of controllable and uncontrollable variables. Controllable variables include: kiln's negative pressure (filter), temperature of kiln body, flame's profile, fuel pressure adjustment, kiln blades and humidity in raw material. Uncontrollable variables include: Air temperature and change of trend in raw material because of mine. Next the FMEA methodology were used to identify and prioritize failure modes in the plaster production process and then necessary actions to eliminate or reduce the occurrence of failure modes were taken and finally the results of the analysis with the aim of creating the full reference for future problems were recorded in the Knowledge base of the designed system.

Knowledge Representation. In this study the rule-based approach has used as a general framework for the knowledge representation. In this research experts' problem-solving knowledge were encoded in the form of IF <Situation> THEN <Action> rules. This set of rules has formed the knowledge base of ES. The knowledge rules have obtained from manuals, procedures, technical documentations and forms of interviews with experienced engineers and technicians. During the interview process, conversations recorded in detail and then transferred to the FMEA worksheet. Finally, a total of 60 rules applied as specific and general knowledge for interpreting X-bar and R control charts.

Implementation. The proposed hybrid model (EDSS) has designed using three main modules. The first module is related to knowledge base, the second module is related to designing the interface and the third module is related to running the system

Conclusions

The main goal of this work was to present the capabilities of emerging technologies and algorithms, dealing with quality issues in shop floor. In this work a hybrid EDSS was designed to support the operators in troubleshooting of plaster production process. For this purpose, ES and NN were integrated via designed interface software for reasoning of deviation sources and recommending corrective actions. The ES tries to determine the fault area as far as possible until the serviceman focuses on the point. On the other hand, in the structure of current model, features of LVQ and MLP networks are used in two modules. Therefore, the competitive power of LVQ network in pattern classification and the interoperability of multi-layer perceptron networks for parameter estimation of abnormal patterns in different process control chart were used simultaneously. This integrated approach has provided an appropriate condition for implementing desired thoughts. Considering the training of the network for identifying basic and mixed significant behaviors, the numerical results in table 5 shows output of module I in diagnosis of behavioral patterns, which is acceptable. The results also show that module II who is estimating the parameters of corresponding patterns, is effective and reliable (Table 3). The work has many contributions and was covering multi tasks including: production, delivery and encryption of NN input, using a wide range of data in training the NNs, and doing most of SPC required via integrated system such as drawing of basis chart, checking X-bar and R charts for being under control and also calculating Cpk. Smartness is key criteria in Industry 4.0 and the case study shows the capability of proposed model to bring more intelligence to the production lines.