Alberto Cottica
email: alberto@edgeryders.eu

Amelia Hassoun
email: amelia.hassoun@oii.ox.ac.uk

Marco Manca

Jason Vallet

Guy Melançon
email: guy.melancon@u-bordeaux.fr

Semantic Social Networks: A Mixed Methods Approach to Digital Ethnography

We propose a mixed methods approach to digital ethnographic research. Treating online conversational environments as communities that ethnographers engage with as in traditional fieldwork, we represent those conversations and the codes made by researchers thereon in network form. We call these networks semantic social networks, as they incorporate information on social interaction and their meaning, as perceived by informants as a group, and use methods from network science to visualise these ethnographic data.

We present an application of this method to a large online conversation about community provision of health and social care and discuss its potential for mobilizing collective intelligence.

Introduction

Treating conversation platforms as communities in which humans engage in communication and meaning-making [START_REF] Rheingold | The Virtual Community: Homesteading on the Electronic Frontier[END_REF], we perform ethnography which generates codes that can be analyzed in network form. We define a special type of network representation of ethnographic data, semantic social networks (SSNs), and argue that a methodology based on them is accountable to ethnography as a discipline. Its steps, save for the final quantitative analysis layer, carry naturally over from traditional field research to the digital domain.

So does ethnography's focus on human communities.

SSNs can help discover collective worldviews, address open-ended questions, and scale reasonably well. They show promise as tools to harness collective intelligence, the production and processing of meaningful information by connected human groups [START_REF] Lévy | Collective intelligence: Mankind's emerging world in cyberspace[END_REF].

We first situate our contribution in digital ethnography and network science literature (section 2). Then we introduce a data model for SSNs (section 3).

Next, we present data in SSN form from a study on community-provided health and social care services (section 4). We then illustrate how we used SSNs to aggregate and navigate a large corpus of ethnographic data (section 5). Finally, we reflect on their potential and possible extensions (section 6).

Related work

Digital Ethnographic Methods

Responding to calls for "multimodal" and "network" ethnography integrating anthropological participant-observation with digital research methods, our ethnographic method maps online conversations to analyze community meaningmaking practices [START_REF] Dicks | Multimodal Ethnography[END_REF][START_REF] Howard | Network ethnography and the hypermedia organization: new media, new organization, new methods[END_REF][START_REF] Murthy | Digital ethnography: An examination of the use of new technologies for social research[END_REF]. Following emerge from the Net when enough people carry on those public discussions long enough, with sufficient human feeling, to form webs of personal relationships".

SSNs encode these webs of relationships.

We treat online conversational environments (such as digital fora) as communities [START_REF] Beaulieu | Mediating ethnography: Objectivity and the making of ethnographies of the Internet[END_REF][START_REF] Miller | The Internet: An ethnographic approach[END_REF]. We visualise informant contributions in a semantic network, like approaches outlined by [START_REF] Bernard | Analyzing qualitative data: Systematic approaches[END_REF], but with significant differences: codes are generated through direct ethnographic coding on an online platform and automatically visualised in network form, in contrast to manual construction of the social network [START_REF] Howard | Network ethnography and the hypermedia organization: new media, new organization, new methods[END_REF]. The semantic network is therefore not generated from keywords, but from sustained ethnographic engagement with user contributions. This approach integrates benefits from QDA software [START_REF] Bernard | Analyzing qualitative data: Systematic approaches[END_REF]; iterative meaning-mapping approaches (Dressler et al. 2005); anthropological participant-observation; and social network analysis to display a map expressing what community members are talking about, and who is talking to whom about what concepts (as interpreted by the researcher). It provides a rich visualization, derived from grounded theory [START_REF] Bernard | Research methods in anthropology: Qualitative and quantitative approaches[END_REF][START_REF] Bernard | Analyzing qualitative data: Systematic approaches[END_REF], to aid in anthropological theorization.

Anthropologists have suggested that network images enhance qualitative understandings of social processes [START_REF] Burrell | The field site as a network: A strategy for locating ethnographic research[END_REF][START_REF] Hannerz | The global ecumene as a network of networks[END_REF][START_REF] Strathern | Cutting the Network[END_REF].

Several mixed methods approaches combine qualitative research with social network mapping. [START_REF] Snodgrass | Online Gaming Involvement and Its Positive and Negative Consequences: A Cognitive Anthropological Cultural Consensus Approach to Psychiatric Measurement and Assessment[END_REF] utilise psychometric measurement techniques and interviews to generate measurable qualitative data sensitive to socioculturally-specific meaning. [START_REF] Dengah | The Social Networks and Distinctive Experiences of Intensively Involved Online Gamers: A Novel Mixed Methods Approach[END_REF] use survey data and "egocentric social network interviews" to theorise the relationship between social support and online gaming involvement. We employ online ethnographic participant-observation rather than offline interviews, and construct social networks from observed, rather than reported, online interaction. Dressler et al. (2005) share our aim: retaining sensitivity to informants' contextual, interactional, and socioculturally-specific understandings of concepts while also introducing systematic means of visualizing (and measuring) them.

We, however, do not to attempt to measure a pre-hypothesised concept like cultural consonance. Instead, we visualise ethnographic research reflecting the contributions of community members, materialised through codes, in a social network and compute indicators on that network.

Semantic social networks

The notions of semantic network and semantic social network have been used in different contexts to denote different concepts. [START_REF] Sowa | Conceptual structures: information processing in mind and machine[END_REF] introduced conceptual graphs as encoding the logical structure of statements, also called "semantic networks" [START_REF] Sowa | Conceptual graphs as a universal knowledge representation[END_REF]). His theory focuses on mathematical patterns and rules, and transformations operated on the graph structure emulating human reasoning [START_REF] Sowa | Conceptual structures: information processing in mind and machine[END_REF][START_REF] Sowa | Knowledge representation: logical, philosophical, and computational foundations[END_REF], mathematically representing knowledge in a manner suitable for deduction [START_REF] Shapiro | Representing and locating deduction rules in a semantic network[END_REF][START_REF] Woods | What's in a link: Foundations for semantic networks[END_REF].

Statistics-based approaches known as text mining competed with Sowa's, focusing on co-occurrence patterns of words in documents. Other computational approaches (like unsupervised learning) suggest how groups of terms reflect the semantic structure of documents, often representing documents as weight vectors [START_REF] Salton | Term-weighting approaches in automatic text retrieval[END_REF][START_REF] Salton | A vector space model for automatic indexing[END_REF]. Graphs are utilized when using document vectors to compute similarities between documents (fragments of conversations). Nearest-neighbor approaches [START_REF] Fukunaga | A branch and bound algorithm for computing k-nearest neighbors[END_REF][START_REF] Kramer | K-nearest neighbors[END_REF]) are often used to induce links between similar documents. Structural characteristics of the graph are interpreted at the corpus level. [START_REF] Jiang | News framing in an international context: A semantic network analysis[END_REF] exemplifies the use of these statistical apparatuses to address problems involving large document collections.

We depart from this largely non-ethnographic literature by constructing semantic data based upon ethnographic coding of community members' conversational themes, elucidated through on-platform interactions with participantobserver researchers. Unlike researchers like [START_REF] Roth | Social and semantic coevolution in knowledge networks[END_REF], we do not use natural language processing techniques. We use SSNs to theorise how humans co-construct meaning through social interaction, rather than how individual behavior produces network structure, or how concepts in more static or single-authored documents like news media relate to each other.

Social networks are traditionally inferred from relations or interactions between people [START_REF] Borgatti | Network Analysis in the Social Sciences[END_REF], represented as graphs where relations are embodied as links between nodes (persons) [START_REF] Wasserman | Social Network Analysis, Methods and Applications[END_REF]. Structural metrics (degree, distances, centralities, matrix eigenvalues, etc.) can be interpreted as socially meaningful [START_REF] Burt | The network structure of social capital[END_REF][START_REF] Freeman | A Set of Measures of Centrality Based on Betweenness[END_REF][START_REF] Scott | Social Network Analysis: A Handbook[END_REF].

We represent ethnographic data as a network. We choose its form so that it encodes information on both what each informant is talking about (as interpreted by the ethnographer), and who she is talking to. So, the network is a semantic and social one -a SSN. It stores the data in a structured form, without compromising their rich, contextual character.

SSNs are for human consumption, and therefore underpinned by a simple, intuitive ontology. Our representation has only two types of nodes, participants in the conversation and ethnographic codes, and three types of edges: comments-the-content-of (connects a participant to another), authored-posts-annotated-with (connects a participant to a code), and co-occurs-with (connects a code to another). By contrast, graph databases (proposed as early as the 1970s) can and do encode many types of relationships [START_REF] Shapiro | Representing and locating deduction rules in a semantic network[END_REF].

A data model for digital ethnography

Ethics

OpenCare's conversation was hosted on an instance of the forum software Discourse (Atwood et al. 2013(Atwood et al. -2019)), where participants shared care experiences, discussed in-person events, and exchanged best practices. Participants passed through a "consent funnel" before posting, answering required questions to ensure they understood the project and risks associated. The forum conformed to the European Union's General Data Protection Regulation.

We committed to radical methodological openness, discussing research and coding decisions on-platform with informants [START_REF] Beaulieu | Mediating ethnography: Objectivity and the making of ethnographies of the Internet[END_REF][START_REF] Mortensen | Blogging thoughts: Personal publication as an online research tool[END_REF].

Ethnographic Coding: Methods and Implications

We employed iterative, inductive, and reflexive coding processes [START_REF] Auerbach | Qualitative data: An introduction to coding and analysis[END_REF][START_REF] Bernard | Research methods in anthropology: Qualitative and quantitative approaches[END_REF][START_REF] Saldaña | The coding manual for qualitative researchers[END_REF][START_REF] Sunstein | FieldWorking[END_REF]).

An anthropologist (Hassoun) coded twenty-eight hours a month over a oneyear period. She performed three coding passes on the dataset, with continuous updating of codes based on a detailed codebook with memos and analysis notes.

Both descriptive and in vivo codes were utilised.

The ethnographer coded directly on-platform via an application we added to the platform's code base. This process created a database of codes and automatically visualised it in network form.

We find ethnographic coding preferable to word count or keywords, because coding is not just a process of labeling, but a clarifying, analytical process in which the ethnographer names and maps the concepts invoked by the community [START_REF] Saldaña | The coding manual for qualitative researchers[END_REF]. Informants use different words to mean similar concepts, making a word count less effective. The analytical process undertaken by the ethnographer produces codes that more richly reflect the communitys collective intelligence, encoding "summative, salient, essence-capturing, and/or evocative" attributes [START_REF] Saldaña | The coding manual for qualitative researchers[END_REF].

We derive codes from ethnographic participant-observation of community members' long-term engagements with each other, analyzing meaning on different levels of analysis. One level is the subjects community members consider most important and how they relate to each other (visible in the entire network of codes. see Figure 1). Another is a more granular focus on specific topics (manifest in a single code or subnetwork of codes). Yet another level is the focus on a selected group of community members (displayed as the network of codes discussed by a selected group, see Figure 2). As contributions and codes are time-stamped, the network of ethnographic codes also allows researchers to see what topics become interesting over time, to whom, and through what kinds of interactions. The ethnographer did not artificially limit the amount of codes assigned (in part so the codes and corpus could be expanded upon in the future), instead mapping codes as accurately as possible onto informants' categories of analysis.

Because we employ grounded theory and had community members participating from a wide range of cultural and institutional contexts, we did not pre-define any codes based on existing theories.

On-platform coding produces a digital codebook, making coding decisions transparent and codes easily editable. Visualizing the semantic network enables iterative coding processes, illuminating which codes might be redundant or need forking. Both elements enable multiple researchers to work on the same, large corpus in a coherent way, with unique identifiers separating each researcher's codes. This makes our method partially scalable. It offers a clear benefit over multiple researchers to use. These methods also make the ethnographer's interpretive process more visible. Coding is a process of reflexive interpretation that requires moving between the positionality of the researcher and the worldviews of informants [START_REF] Rosaldo | Culture and Truth: Remaking of Social Analysis[END_REF]). Keeping a detailed, open codebook and memos makes this more transparent than in traditional ethnographic studies. These coding practices and visualisations utilise ethnography's ability to tease out collective beliefs and practices while rendering the researcher's situatedness and partiality visible. Future studies employing multiple ethnographers will enable comparison of SSNAs generated by different researchers, further shedding light on this interpretive process.

Contributions

SSN-based ethnographies start with posts/comments on the social networking platform. We call contribution a testimony in written form (interview transcript, post on an online forum, etc.). A minimum viable structure for encoding a contribution as primary data includes:

Contribution ID The contribution's unique identifier.

Text The contribution's complete text.

Author ID A unique identifier for the informant that contributed the text.

Target ID A unique identifier for the informant that the text is addressed to.

Date and time

Annotations

Ethnographers associate snippets of texts in contributions to keywords, called codes. This generates an ontology representative of the corpus. We call annotation the atomic result of this activity. A minimum viable structure for annotations includes:

Annotation ID The annotation's unique identifier.

Contribution ID The unique identifier of the post or comment that this annotation refers to.

Snippet The part of the text in the contribution that the researcher wishes to associate with the code.

Code The ethnographic code associated to the snippet.

Author ID Unique identifier for the researcher that produced the annotation.

Date and time

This representation induces a network where the nodes are informants and edges represent interactions. Codes -associated to the interaction via annotations -encode the semantics of that interaction. We call this an SSN. We propose it is general enough to fit evidence from most ethnographies, while structured enough to be encoded into a dataset.

An application: the OpenCare data

The OpenCare project explores how communities provide health and social care when neither states nor businesses can or will serve them. We began with the research question: What do people do when existing health and social care systems no longer provide care? Data were gathered from an online forum where individuals discuss their care experiences. We used the method described in section 3 to code them and build the corresponding SSN. We then built a social and a semantic network from the coded data.

The OpenCare social network

Online conversation induces a social network where nodes are community members and edges encode interaction. For two users A and B, we induce a connection A → B if A has commented B's content at least once. This network is directed edges.

The main feature of this network is a clear core-periphery structure. Almost all participants are connected to the giant component, so information can flow freely across the network. The giant component itself is not obviously resolved into distinct sub-communities (its modularity value [START_REF] Newman | Finding and evaluating community structure in networks[END_REF] is 0.38). These structural features allow us to infer that most opinions expressed in the forum have been expressed in a public space that everybody participates in. There are no signs of isolation of individuals, nor of balkanization of the conversation.

SSNs can also be represented in ways that emphasize the semantics of the online conversation. The representation that proved most useful to ethnographic research is what we call the co-occurrence network. Its nodes are codes. Whenever two codes occur in annotations that refer to the same post, they are said to cooccur in the same post, and an edge is induced between them. This network is undirected (A → B ≡ B → A) and weighted (the edge has a weight of k if A co-occurs with B on k different posts or comments). We can think of the co-occurrence network as an association map between the concepts expressed by the codes. A higher edge weight k indicates a stronger group-level association between the two codes connected by the edge.

The annotations on the OpenCare corpus induce a co-occurrence network with 1,248 nodes, and 16,727 edges. The main component is formed of 1,234 nodes and 16,702 edges, and shows a small-world structure [START_REF] Watts | Collective dynamics of 'smallworld' networks[END_REF] with a high average clustering coefficient C = 0.696.

Filtering the co-occurrence network for a high-level view

Rather than representing the point of view of an individual, the co-occurrence network encodes contributions from informants as a group in conversation, as interpreted by an ethnographer. The resulting concept map, therefore, does not simply aggregate the association patterns of individuals, like a survey; it is the product of the interaction across participants. Edge weight k, then, represents the strength with which the conversation associates the codes connected by that edge.

Filtering the graph by higher value of k allows the researcher to see the strongest associations between codes made by informants as a group. She can experiment with different values of k, starting from a low value and increasing until the graph simplifies enough to be interpretable. For the OpenCare dataset, filtering edges by k ≥ 6 yields a co-occurrence network with 60 codes and 72 edges, which lends itself well to visual inspection (Figure 1).

From it, one can see the structure of community members' concerns. Consider the cluster with legality (in green): we find existing system failure and regulation, reflecting the preoccupation of some informants that community health care initiatives and technological innovations outside of existing systems (much needed when systems fail), turn out to be illegal and therefore difficult to implement. We also find safety, reflecting the acknowledgement that regulation is often there for a reason.

We can also see isolated but intense conversations visualised as islands in the high co-occurrence network: the network death, grief, and (visible at a lower co-occurrence level, online memorials) appear unconnected to the rest of the network, indicating a deeply discussed single issue. In this case, community members intensively discussed this issue on one highly active thread, but the topic was not discussed more widely across the platform. In the next section we describe how to tell if a discussion is driven by a small number of community members or a larger group.

A researcher can choose to look at the network of associations around a topic of interest at a more granular level by clicking on the link between two codes to view all community contributions containing both codes (like design intervention and cost reduction) to see what specific innovations the community has devised.

The method allows for rich analysis on multiple levels, retaining the granularity that makes ethnographic research so powerful. High co-occurrence edges in the semantic network illuminate connections that might be invisible at a smaller scale of analysis, allowing the ethnographer to visualise and understand her informants' concerns and how they relate to each other. Without the co-occurrence network, vital interconnections made by informants would have been missed; without the detailed ethnographic data, the meaning behind those connections would be lost.

A detailed discussion is out of scope of this methods-focused paper, but in the OpenCare project this method lead to key insights into informants' beliefs, desires, innovations, and concerns. Centrally, people, facing the collapse of existing health and social care institutions, reach for what we term "collective autonomy": feeling empowered to solve their own problems while in a communitybased framework [START_REF] Hassoun | Collective autonomy. An ethnography of open care services[END_REF]. This finding has clear implications for states and non-governmental organizations trying to help people in crisis. Care solutions that treat people as helpless or remove them from a community context will likely fail. Refugees wanted the tools to collaboratively build their own temporary living spaces and markets rather than being infantalized; mental health patients found helping others in their community therapeutic. Solutions that connected people with others with compatible skills, gave them tools and space to experiment, or strengthened care networks in communities were most useful to people seeking care outside of existing health and social care systems. Some of the network's properties have straightforward interpretations and can be used to validate or extend the researcher's conclusion. A researcher can use edge weight to get a precise idea of how strong the association between any two codes is. She can also use community detection algorithms to get a quantitative indicator of how neatly a problem resolves into sub-issues. We applied to the network in Figure 1 the Louvain community detection algorithm [START_REF] Blondel | Fast unfolding of communities in large networks[END_REF]: it is highly modular (with a modularity of 0.64) and presents clearly distinguishable communities of codes, identified by color.

Enriching semantic information with social network structure information

The OpenCare social and semantic networks, as described in sections 4.1 and 2.2, are interlinked by the data structure defined in section 3. This enriches semantic information with information on the structure of the social network.

For example, we can check that the social network underpinning any one edge in the semantic network is connected. A connected social network signals that informants who have made the connection between those two codes are in conversation with each other: they are aware of each other's existence and have had the opportunity to interact around that particular connection to arrive at an interpretation of its nature and importance for the problem at hand. A disconnected one signals that they never conversed at all: they agree the two codes are connected, but might have different interpretations of that connection unimpacted by each others' views. In Figure 2, there are four informants who have mentioned both smartphone-based and healthcare app (6 co-occurrences), and they are not interacting directly with each other. There are eleven who have mentioned both legality and existing system failure (9 co-occurrences), and they are all connected in a dense network of direct interaction. The latter association has the potential for being supported by a consensus resulting from the conversation, not unlike what happens in Wikipedia [START_REF] Laniado | When the wikipedians talk: Network and tree structure of wikipedia discussion pages[END_REF]; the former does not. The payoff of such a shift is substantial. We could imagine a version of Eurobarometer based on an open online conversation. Instead of answering multiple choice questions, vulnerable to framing biases [START_REF] Tversky | The framing of decisions and the psychology of choice[END_REF], informants would discuss their perception of Europe, allowing researchers to discover novel patterns of association and detect the fading of old ones.

Conclusions and future improvements

Our method could be further improved along the following lines:

1. Develop the idea, introduced in section 5.2, of applying existing social theory on the social network topology to derive "interest scores" on individual informants and connections in the semantic network.

2. Apply alternative ways to measure edge (association) strength k in the cooccurrence network. For example, k(A → B) could encode the number of informants that have authored contributions coded with both codes A and B, or the number of separate threads which contain at least one contribution with it. Different measures of edge strength have different interpretations, so they allow different perspectives on the data corpus.

3. Observe and model the online conversation as a dynamic system. Stochastic Actor-Oriented Models might be a good place to start, despite known limitations [START_REF] Snijders | Stochastic actor-oriented models for network change[END_REF].

4. Multilingual studies: The code ontology can be structured as a hierarchy, so that codes with the same meaning in different languages are entered in the secondary data as children of the same parent code. The code cooccurrence network would be drawn between parent nodes, allowing both an all-languages view on data and across-languages comparisons. This work would allow for analysis of generalizability of codes across languages (and potentially different cultural understandings of concepts, although cultural difference would need to be explicitly theorised as an object of analysis).

5. Ethnographic coding as compression: empirically, in OpenCare, the rate at which new codes are produced declined, approaching zero. This suggests that, after a certain number of iterations, the online conversation produced an ontology (nearly) sufficient to code all future contributions. This phenomenon is reminiscent of file compression in computer science. [START_REF] Ziv | A measure of relative entropy between individual sequences with application to universal classification[END_REF] If the analogy holds, a dynamic analysis of the conversation could identify moments of significant evolution by spikes in the growth rate of the codes dictionary as new ideas are suddenly generated. We could estimate the overall relatedness and novelty of conversations. We intend to refine and test this hypotheses on more corpora of similar size and scope.

Fig. 1 :

 1 Fig. 1: The OpenCare code co-occurrence network (filtered for k ≥ 6).

  Fig. 2: Two edges in the OpenCare semantic network (left) and their associated social networks (right). Structural differences in the latter hints to different degrees of convergence in how the online conversation interprets the former.

We have released the OpenCare dataset as open data: https://doi.org/10.5281/ zenodo.164970; https://github.com/opencarecc/opencare-data-documentation

Acknowledgment The OpenCare project received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement 688670).

on Information Theory, 4(39), 1270-1279.


Filipa Ferrada

Ana Inês Oliveira

João Rosas

Patrícia Macedo
email: patricia.macedo@estsetubal.ips.pt

Ricardo Almeida
email: rdl.almeida@campus.fct.unl.pt

Luis M Camarinha-Matos

Development of a Conceptual Architecture for the Energy Management of Building Ecosystems

Keywords: Energy efficiency of buildings, Conceptual architecture, Building ecosystem

The current environmental, social, and political pressure for more efficient use of energy led to the emergence of new legislation and directives both at National and European levels and, as a consequence, to the creation of various focused technological initiatives. In this line, several developments are being carried out to increase energy efficiency by reducing the energy consumption of buildings, costs, and carbon emissions, while providing adequate levels of comfort to the building's occupants. This work intends to contribute to this area, by proposing a conceptual architecture, based on collaborative networks of physical components/devices virtualized as services to compute and communicate information to support efficient energy management of building ecosystems. The architecture is designed considering the functional requirements, the most relevant technological challenges, and the implementation strategies.

Introduction

One of the current topics of attention in our society is the large energy consumption and its self-sustainability, making it clear that it is necessary to increase energy efficiency, reducing energy consumption and carbon emissions in this sector [START_REF] Dgeg | Estratégia para a Eficiencia Energética nos edificios Públicos[END_REF] [START_REF] Dgeg | Balanço Energético, sintético[END_REF]. In this context, it becomes essential to address these problems in industry and service buildings (public and private), checking the main energy efficiency gaps, and intervening to rectify the main issues in building's equipment that contribute to inadequate energy consumptions.

On the other hand, the need for environmental comfort in buildings often comes into conflict with the awareness of reducing energy consumption, creating the need to find solutions that integrate the comfort of the occupants, the indoor air quality (IAQ) and efficient energy management, and consequently, the level of carbon emissions. Some building energy management systems (BEMS) already consider these issues, nevertheless the costs for implementing such systems are still quite high and with no capacity to scale or adapt. Environmental comfort in buildings requires a transformation of BEMS to accomplish some objectives, namely, energy efficiency and occupants' comfort, scalability and interoperability of systems, and reduced cost of implementation. In addition, as sub-systems / components become more intelligent, embedding higher computational power, a promising approach is to consider the building as an ecosystem of collaborative sub-systems.

In this context, a conceptual architecture for building energy management with low implementation cost, high adaptability, versatility, and easy maintenance is proposed and described. This architecture, which is based on collaborative networks of smart physical components/devices, considers the management and implementation of a set of previously defined environmental quality parameters and energy performance indicators which, in conjunction with machine learning capabilities, supports efficient energy decision-making leading to effective energy management. The approach corresponds to a case of collaborative cyber-physical system and is aligned with the premises of Industry 4.0.

This work is developed in the context of the project AMBIOSENSING -Autonomous and Intelligent Systems for Energy Saving,

The remainder of this paper is structured as follows: section 2 presents the current research trends in the energy efficiency area; in section 3 an overview of business requirements of the system is presented with the identified functional and nonfunctional requirements along with a use case diagram; section 4 presents the proposed conceptual architecture; in section 5 a discussion regarding the implementation aspects is provided, and finally section 6 concludes and highlights future work.

Energy Efficiency Research Challenges

Regulatory Framework

The topic of energy efficiency has become increasingly relevant in Europe and its Member States, which have been working on updating and harmonizing the main European policy instruments in this area. The buildings sector in Europe accounts for around 40% of total energy consumption [START_REF] Masson-Delmotte | Global warming of 1.5°C An IPCC Special Report[END_REF]. However, it is estimated that more than 50% of this consumption can be reduced by implementing energy efficiency measures, which can represent an annual reduction of CO2 emissions to the atmosphere of around 400 million tons. This represents almost the whole of the European Union's (EU) commitment under the Kyoto Protocol [START_REF] Union | Energy efficient buildings -Presidency secures provisional deal with European Parliament[END_REF], [START_REF] Commission | Clean Energy for All Europeans[END_REF]. One of the short-term measures to put in practice the reduction of energy consumption relies on renovating existing buildings. Annual investments in building renovation will have to triple to reach the 32.5% energy efficiency target by 2030.

In addition, energy efficiency is one of the most cost-effective means of supporting the transition to a carbon-neutral economy and a key policy for implementing the Paris Agreement [START_REF]PARIS AGREEMENT[END_REF] to try to limit GHG emissions to a level that limits global temperature increase to less than 1.5°C and preventing catastrophic and irreparable effects on ecosystems. While environmental commitments are being met, energy efficiency also reduces energy bills and import dependency.

Energy efficiency is thus one of the pillars of carbon neutrality strategies of the European Union. In this context, the European Commission (EC) adopted on 28 November 2018 its strategic vision for surpassing the 2030 climate target and to set eyes on achieving a net zero carbon economy by 2050, through what it called a fair transition involving all sectors of the economy [START_REF]Our Vision for A Clean Planet for All[END_REF]. The transition to a carbon-neutral economy is possible from a technological, economic and social points of view, but requires a profound societal and economic transformation within a generation.

In this context, the Directive (EU) 2018/844 of the European Parliament and of the Council [START_REF]Directive (EU) 2018/844 of the European Parliament and of the Council of 20 May 2018 amending Directive 2010/31/EU on the energy performance of buildings and Directive 2012/27/EU on energy efficiency[END_REF] establishes a set of measures and guidelines, some of which are briefly highlighted here: ▪ Promote research and testing of new solutions to improve the energy performance of buildings; ▪ Consider the installation of self-regulating devices in existing buildings; ▪ Use of smart readiness indicators for intelligent technologies, namely in measuring the capacity of buildings to use information and communication technologies and electronic systems to adapt the operation of buildings to the needs of the occupants; ▪ Provide effective inspections of the central heating systems and air-conditioning systems, including those which are combined with ventilation systems, to obtain better energy efficiency results; ▪ Provide new buildings with self-regulating devices to be able to regulate the comfort conditions in each room of buildings and facilities, autonomously. The architecture proposed in this work takes the above policies and measures into consideration.

Building Energy Management Systems

The building energy management systems (BEMS) are responsible for monitoring and controlling the environment in commercial, industrial and institutional facilities, ensuring operational performance as well as the comfort and safety of buildings' occupants. These systems usually use computer networks to monitor and control the energy of services and equipment in buildings, such as Heating, Ventilation, and Air Conditioning (HVAC) systems, lighting systems, energy systems, pumping systems, among others [START_REF] Gromicho | Protocolo de Quioto continua a ser essencial para países reduzirem emissões[END_REF].

BEMS are often confused with Building Management Systems (BMS) and Building Automation and Control Systems (BACS), however, in practice they are not the same. BMSs can be used to monitor and control a wide range of existing building systems including fire, smoke detection, alarms, CCTV, security and access control, elevators, among others, while the BACS focus on management and control and automation processes. On its turn, BEMS are specifically related to energy systems. They can, however, be connected to one or both systems if necessary. BEMS can combine data from all systems to provide better and more accurate visualization of energy use and consumption.

BEMSs provides real-time remote monitoring and integrated control of a wide range of connected systems and devices, providing information on modes of operation, energy use, environmental conditions, maintenance, among others, including measurement, notification trend and diagnosis of unnecessary energy use; performance optimization by setting operating hours, set-points, among others; preventive and corrective actions and building comfort [START_REF]Directive (EU) 2018/844 of the European Parliament and of the Council of 20 May 2018 amending Directive 2010/31/EU on the energy performance of buildings and Directive 2012/27/EU on energy efficiency[END_REF]. BEMSs also allow the recording of performance history, enabling performance benchmarking about other buildings or locations. The best energy and comfort management systems in buildings should also add external information such as service's billing, electricity network, and meteorological data.

Energy Monitoring Systems. These are systems capable of measuring the consumption of one or more electrical equipment in a building. They emerge from the need to reduce unnecessary energy consumption to achieve better energy efficiency. These systems quantify and identify the consumption of energy in real time of an area or location and determine the associated economic impact. They also provide the identification of abnormal consumption, facilitating its correction.

There are several energy consumption monitoring systems on the market, each with its characteristics, but not all of them respond to the same type of needs. Some are more dedicated to the analysis of electrical parameters while others are limited to reading remote data. Table 1 presents a comparison of various monitoring systems according to the identified characteristics. Control and Automation Systems in Buildings. Also known as building automation systems, these systems have undoubtedly led to the general specifications of monitoring and measurement systems. These control systems are generally centralized and integrate software and hardware networks that monitor and control indoor weather conditions. The operational performance of buildings, as well as the safety and comfort of occupants, is usually ensured by these control systems.

Existing building control systems can be roughly categorized as conventional controllers and intelligent controllers. The conventional ones focus essentially on energy consumption, being the best known the on/off controllers (better known as thermostats), and the P, PI and PID controllers [START_REF] Solutions | ViGIE 2.0[END_REF], [START_REF] Lem | Wi-LEM[END_REF], [START_REF] Manic | Building Energy Management Systems: The Age of Intelligent and Adaptive Buildings[END_REF]. On its turn, the intelligent controllers besides the energy consumption, also consider the comfort of the occupants in buildings. Examples of intelligent controllers are: methods based on learning that include artificial intelligence, fuzzy systems and systems of neural-fuzzy networks with conventional controllers, adaptive neural-fuzzy inference systems (ex. ANFIS -Adaptative Neural Fuzzy Inference System), etc. [START_REF]Directive (EU) 2018/844 of the European Parliament and of the Council of 20 May 2018 amending Directive 2010/31/EU on the energy performance of buildings and Directive 2012/27/EU on energy efficiency[END_REF], [START_REF]SiteSage (Previously eMonitor) Energy Monitor[END_REF]; models based on predictive control techniques (MPC), which follow the principles of classical controllers [START_REF]Features of the Optimal Energy Monitoring Software System[END_REF]- [START_REF] Novalec | BeEnergy -Monitorização de energia[END_REF]; and control models based on agents [START_REF] Commission | Smart Finance for Smart Buildings Investment Facility[END_REF], [START_REF] Levermore | Building Energy Management Systems: An Application to Heating and Control[END_REF].

A more in-depth analysis has been carried out about the products supplied by various manufacturers. This study was done by selecting the products that have functionalities relevant to energy efficiency. The information was obtained online, through the manufacturer's commercial web pages, as well as from product/solution brochures, when available. This was a difficult task given the natural and understandable resistance from the manufacturers to reveal the most technical details of their products. In this sense, some technical information may be omitted due to lack of available information. Nevertheless, the solutions and products analyzed provide a wide range of alternatives for the control and energy optimization of buildings. Due to space constraints, the detailed analysis is not presented. However, it can be said that the predictive control models have gained popularity, especially in recent works, being the most common model used by most of the reviewed products and literature. Predictive control can be explained having into account that this type of model, grouped under the name of Model Predictive Control (MPC), consistently have better performance levels than other optimization and control algorithms. These models have proved their value with their ability to adapt to: (i) unpredictable disturbances or errors in prediction; (ii) exploration of the thermal mass of a building; (iii) consideration for variable energy prices, and; (iv) ability to protect the load against voltage peaks. New generations of intelligent buildings should not only take into account aspects such as space occupation prediction and meteorological conditions but also be sufficiently adaptable to maximize the use of renewable energy sources, energy storage units and program their consumption around periods of low energy cost [START_REF] Shaikh | A review on optimized control systems for building energy and comfort management of smart sustainable buildings[END_REF].

Currently, some commercial building energy management systems [START_REF] Vishwanath | IoT and Machine Learning to Reduce Energy Use in Cooling Systems[END_REF], [START_REF] Electrics | Control Systems[END_REF] already started to cover some of the intelligent aspects described above, nevertheless, they are still in an embryonic stage. Moreover, the costs associated to these systems are still quite high and with no capacity to scale or adapt. In this line, the proposed architecture aims to cover intelligent techniques aspects, such as optimization algorithms, with low implementation cost, high adaptability and scalability, and easy maintenance.

Requirements in Energy Management of Building Ecosystem

The identification of requirements, from the end user's point of view, is a key point in the process of design and development of a system. For the specific case, to identify the main functionalities that the system should provide, a set of potential usage scenario have been foreseen for its usage, namely: a factory of cosmetic production, a greenhouse for flower production, an anchor store, and a call center. Table 2 presents the identification of the main functional requirements of the system, while Table 3 shows the main non-functional requirements.

Table 2. Identification of the main functional requirements of the system

Functional requirements (FR)

[requirements related to technical functionalities or system performance]

FR1

The system must be able to define the environmental variables to be controlled: CO2, Humidity, Temperature, Particles, COV, and Brightness, etc.

FR2

The system shall allow the configuration of several sensors and the connection of the sensors to environmental variables.

FR3

The system must allow configuration and connections of actuators and actuation systems (valves, switches, blades, etc...).

FR4

The system must allow integrated control of environmental variables.

FR5

The system must allow the replacement of sensors or actuators while the system is running.

FR6

The system must allow energy usage profiles to be defined and its control should be adjusted accordingly.

FR7

The system shall withstand operation in automatic mode and manual mode. In manual mode, the energy profile is selected manually (RF6), in automatic mode, the profile is automatically selected by the system to optimize its performance.

FR8

The system should allow the simulation of different energy profiles by creating scenarios.

FR9

The system must allow remote supervision, supporting real-time monitoring and alarm configuration.

FR10

The system must support behavioral analysis.

FR11

The system must allow historical data consultation.

FR12

The system must support the generation of reports on its performance.

Table 3. Identification of the main non-functional requirements of the system

Non-functional requirements (NFR)

[requirements related to the description of system performance]. NFR1 Availability: The system must be operational at least 90% of the time.

NFR2

Accessibility: The system must provide user interfaces for mobile and non-mobile devices.

NFR3

Performance: the system should allow efficient intelligent supervision and optimization of energy efficiency considering the comfort of the occupants.

NFR4

Performance: the system must efficiently support the management of large and complex data sets. NFR5 Maintenance: the system must have a set of processes to allow real-time monitoring, with mechanisms to recover from failures/errors, such as data loss and communication failures. NFR6 Scalability: the system must allow the introduction and removal of new measurement devices and actuation systems.

NFR7

Interoperability: the system must support the integration of heterogeneous systems (e.g., IoT solutions, legacy systems, etc.), with different degrees of intelligence / autonomy.

NFR8

Storage: the system must allow the storage of a large volume of information.

NFR9

Cost: the implemented solution must use non-proprietary and freeware software and equipment, throughout its life cycle.

Given the heterogeneity of all the elements in terms of energy management resources and services, the architecture for such a system should leverage solutions that enable these elements to be organized into collaborative ecosystems [START_REF] Brettel | How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective[END_REF] in which they can understand each other, exchange information, and collaborate. Therefore, there are two main phases for such a system, including the installation and exploitation.

Fig. 1. Use case diagram of the exploration phase of the Ambiosensing system

For the installation phase, the main user of the system is an Installer, who will perform the entire configuration of the energy management system. There is another user, the Administrator, that is present in the whole life cycle of the system and is in charge of making the registration of all users.

In the exploration phase, different profiles of System Users can exist, namely: the Administrator, the Technical Manager, and the Occupant of the installations. The Administrator is allowed to register users and has access to the functionalities that allow global analysis of the Energy Management System (generation of reports, consultation of historical data, simulations, etc.), but also has access to all functionalities of the System available to the Technical Manager. The Technical Manager is the user responsible for daily operation and maintenance, being able to monitor all the controlled environmental variables and associated equipment and change the basic configurations of the energy control. The Occupant can introduce preferences into the System, as well as report his/her degree of satisfaction regarding environmental comfort. The main use cases for the exploitation phase are illustrated in Fig. 1.

Conceptual Architecture

The conceptual architecture (or high-level view) aims to show succinctly, the main elements and functionalities that make up the system architecture, regardless of any technological specificity. Based on the specification of the use cases, a conceptual architecture for the Ambiosensing system was developed as illustrated in Fig. 2. A layered architecture is proposed to identify and separate the different levels of interaction and to better manage dependencies. Each layer has a specific responsibility, being the upper layers able to use services from the lower layers, but not the opposite. Thus, in the case of the Ambiosensing System, three layers have been defined as follows below. Base Platform Layer, which is composed of two sub-layers: the Support Infrastructure Layer and the Resource Management Layer. The main objective is to provide cloud services to support the development and management of IoT functionalities (intelligent networks of sensors and/or remote devices, actuators, devices, automation mechanisms, etc.), materializing digital twins, and the necessary functionalities of the Ambiosensing system. Specifically, it aims to support: the installation of sensors and actuators via standard communication protocols and their representation as digital twins; and management services for the processing of large volumes of data from multiple sensors and actuators of various types, such as variable speed drives, luminosity sensors, presence, etc., being all these devices modeled as software service. This layer is also intended to support the storage and management of data/information inherent to the Ambiosensing system as well as access mechanisms to them. The invocation and composition of software services, as well as data collection, control of environmental variables, management of equipment and devices and also the management of existing software services in the Ambiosensing environment, is also included. Through this platform, the various physical components are seen as a community of collaborative digital twins (a kind of collaborative cyber-physical system) [START_REF] Rodič | Industry 4.0 and the New Simulation Modelling Paradigm[END_REF], which are abstracted through the services they provide and their collaboration capabilities. Fig. 3, illustrates a building floor scenario where several sensor units (assuming that sensors are not intelligent) are physically connected to a single board computer (Ambiosensing intends to adopt the Raspberry Pi model for prototyping) which on its turn makes part of a collaborative network of single board computers which operate in a coordinate way in order to collaboratively compute and communicate raw and/or processed data to the Ambiosensing IoT based platform. Ambiosensing can thus be seen as a collaborative cyber-physical system [START_REF] Nazarenko | Basis for an Approach to Design Collaborative Cyber-Physical Systems[END_REF]. Service Layer, which is composed of two sub-layers at the same conceptual level: the Supervision Services Layer and the Advanced Services Layer. The first aims at providing support services for supervision, analysis and decision support to allow the correct operation of the Ambiosensing system by its users. It must also allow, through basic management, the configuration and parameterization of the desired operating profiles. To accomplish this objective, the supervision services shall be capable of monitoring, interpreting and analyzing the correct operation of the Ambiosensing system, considering the normal operating parameters. These parameters must consider the operating profile desired by its users. Authorized users shall be provided with tools to support analysis and decision support considering instantaneous monitoring results and historical data. The advanced services sub-layer has the main objective to provide advanced monitoring and decision-making support services that enhance the proper functioning of the Ambiosensing system, taking into account the composition of the underlying digital twins ecosystem. Based on the acquisition of information, it should be possible to extract knowledge about the proper functioning of space with the Ambiosensing system and adapt its functioning within desirable parameters. To this end, services capable of interpreting the information extracted from the operation of a space with an Ambiosensing system should be developed, verifying its operation and analyzing consumption. Techniques and methodologies of machine learning should be used for the analysis of usage patterns to adapt to the general functioning of the building to improve its efficiency. For this purpose, information visualization mechanisms based on simulation of scenarios may be considered.

Ambiosensing Applications Layer: including interaction applications with the various stakeholders that allow the execution of the use cases previously identified in this paper. regardless of its manufacturer, a climate control service will be able to interact with any type of temperature sensor.

The current stage of this ongoing project was validated by our industrial partners and considering its compliance with the identified target application scenarios.

Conclusions and Future Work

Throughout this work, a description of the proposed conceptual architecture for the energy management of building ecosystems was carried out. This architecture emerges as a response to several factors which are considered simultaneously, namely, the diversity of technological resources that are typically used in efficient energy management systems, the need for these resources to exchange information and need to work together. Other considered important factors are the challenges underlying the legislation recently approved by the EU in terms of energy efficiency management.

In terms of the developed work, an exhaustive survey of functional requirements was initially carried out, which is briefly represented in the use-case diagram. The proposed architecture is composed of a significant number of elements distributed over three layers, namely, the base platform layer, the services layer, and the application layer.

The architecture elements aim to cope with the functional and non-functional requirements as well as with the EU directives and policies in the area of energy efficiency. For instance, the "Self-adaptive Energy Performance Management" block meets one of the guidelines approved in the EU. The strategy consists in being able to adapt the operating conditions through intelligent services that identify specific circumstances in building divisions and react to them. In this way, the system will be able to adapt and update its operating energy profile to the circumstances detected. Another aspect that is taken into consideration is the current technological trends, such as industry 4.0. In this line, a simulation element is considered [START_REF] Brettel | How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective[END_REF] [START_REF] Rodič | Industry 4.0 and the New Simulation Modelling Paradigm[END_REF] due to its potential to, among others, study/anticipate the behavior of simulated systems. The proposed architecture includes an element for the simulation of scenarios, to anticipate the effects of certain management strategies and assess their quality.

The concept of collaborative business ecosystem [START_REF] Brettel | How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective[END_REF] and the notion of digital twins [START_REF] Rodič | Industry 4.0 and the New Simulation Modelling Paradigm[END_REF], [START_REF] Nazarenko | Basis for an Approach to Design Collaborative Cyber-Physical Systems[END_REF] to represent the physical systems in the cyber space give the base conceptual framework for the proposed system.

As future work, it is intended to instantiate the architecture in collaboration with our industrial partners, building a platform that will be used and tested in a controlled environment for proof of concept of operation of the system.

Fig. 2 -

 2 Fig. 2 -Detailed diagram of the Ambiosensing architecture.

Fig. 3 -

 3 Fig. 3 -An illustration of a collaborative network of intelligent devices of a building floor.

Table 1 .

 1 Comparison of various energy monitoring systems

		ViGIE	Wi-	SiteSage	Optimal	EMC	BeEner
		2.0	LEM	[12]	Monitoring	[14]	gy [15]
		[10]	[11]		System [13]
	Automation functionality						
	Multi-parameters monitoring						
	Reporting						
	Cost and consumption detailed analysis						
	Wi-Fi communication						
	Alerts generation						
	Use of energy in multiple sites benchmarking						
	Historical data						
	Centralized management						
	Proprietary (integration only with own equipment)						

Implementation AspectsThe proposed architecture presents a set of important aspects that should be highlighted. Such elements are relevant for future instantiations of this architecture and cover three dimensions, namely, (1) technological, (2) strategic, and (3) regulatory and social. For this paper purpose, only a rough description of the technical aspects is explored.Typically, an energy management ecosystem involves a variety of different technologies. To deal with the inherent complexity, a service-oriented approach (SOA) should be adopted to maintain the desired level of abstraction of the use of functionalities versus their respective implementations. In this way, platforms should, as far as possible, be agnostic about the technologies used.This heterogeneity of technologies imposes major challenges in terms of interoperability. Typically, in this kind of systems, elements need to work together to be able to satisfy their functionalities, and a good amount of effort is put into programming appropriate interaction mechanisms. Alternatively, to deal with this complexity, it is suggested the development and use of ontologies[START_REF] Zhou | Ontology development for unified traditional Chinese medical language system[END_REF]. Ontologies allow the various elements, involved in an energy efficiency scenario, to share a standardized set of terms for referencing the equipment and its respective control, including the interactions performed by and between the respective services, thus facilitating collaboration among digital twins. Having the services of a platform developed with these ontologies as a reference, any management element in the layer above should be able to interact with the elements below, achieving in this way a level of integration and quasi-agnostic interoperability. For instance, it is intended that

Acknowledgments

. This work was funded in part by the Project AMBIOSENSING -Autonomous & Intelligent Systems for Energy Saving -nº. 038253, within the specific scope of the Incentive Scheme for Research and Development -PORTUGAL 2020launched by Nº 01/SI/2018. Partial support also comes from the Portuguese FCT Strategic program UID/EEA/00066/2019.


Domenico A Maisano
email: domenico.maisano@polito.it

Dario Antonelli
email: dario.antonelli@polito.it

Fiorenzo Franceschini
email: fiorenzo.franceschini@polito.it

Assessment of Failures in Collaborative Human-Robot Assembly Workcells

Keywords: FMECA, Distributed manufacturing systems, Human-Robot collaboration, Failure-mode assessment

Collaborative Human-Robot workcells introduce robot-assisted operations in small-volume production or assembly processes, where conventional automation is noncompetitive. Unfortunately, the collaborative work of humans and robots sharing the same work area and/or working on the same assembly operation may pose unprecedented problems and failure risks. Failure Mode, Effects and Criticality Analysis (FMECA) is a popular tool to design reliable processes, which investigates the potential failure modes from the perspective of severity, occurrence and detection. The traditional FMECA approach requires the assessment of failure modes to be carried out collectively by a group of experts. Nevertheless, in the field of Human-Robot collaboration, experts are often unlikely to agree in their judgements, due to the almost inexistent historical records. Additionally, the traditional approach is not appropriate for decentralized production/assembly processes. The paper revisits the traditional approach and integrates it with the ZMIItechnique -i.e., a recent aggregation technique developed by the authorswhich overcomes some limitations, including but not limited to: (i) arbitrary categorization and questionable aggregation of the expert judgments, (ii) disregarding the variability in these judgments, and (iii) disregarding the result uncertainty. The description is supported by a real-life application example.

Introduction and Literature Review

Human-Robot Collaboration (HRC) is one of the more significant enabling technologies in the Industry 4.0 framework. Several countries adopt supporting policies to boost the upgrade of existing machine tools and robots with new collaborative models compliant with Industry 4.0 guidelines [START_REF] Almada-Lobo | The Industry 4.0 revolution and the future of manufacturing execution systems (MES)[END_REF].

The technical issues raised by the introduction of humans and robots in the same workspace have been solved by robot manufacturers. Safety standards specific for HRC have been defined (ISO/TS 10218 and ISO/TS 15066) [START_REF]Robots and robotic devices -Safety requirements for industrial robots[END_REF][START_REF]Robots and robotic devices -Collaborative robots[END_REF]. Precision, accuracy and repeatability of HRC are in line with most industrial requirements [START_REF] Antonelli | Qualification of a collaborative human-robot welding cell[END_REF].

Nevertheless, there are still some unsolved problems that are strictly linked to the concept of HRC. First, there is a diffuse lack of confidence in the robot as a teammate. Furthermore, mistakes in the communication between human and robot may represent an unprecedented source of problems and failure risks for the process. It is therefore necessary to develop proofing methods that neutralize the most critical problems.

Common risk analysis methods applied to robotic workcells are Fault Tree Analysis (FTA) and Failure Mode and Effects and Criticality Analysis (FMECA) [START_REF] Gopinath | Risk Assessment Process for Collaborative Assembly -A Job Safety Analysis Approach[END_REF]. There is consensus among authors that both of them are not immediately applicable because the information of the risks cannot be estimated at this stage. Additionally, FTA can only be applied with the support of history of preceding similar processes. This is not the case of the study, as HRC is a new process non-experimented before. This paper focuses on the FMECA, which is a very popular technique to improve the reliability of products, services and manufacturing processes, by analyzing failure scenarios before they have occurred and preventing the occurrence of causes or mechanisms of failures [START_REF] Stamatis | Failure mode and effect analysis: FMECA from theory to execution[END_REF]. Applied to manufacturing processes, the FMECA is very useful to improve reliability and safety and provide a useful basis for planning the corresponding predictive maintenance [START_REF] Johnson | A study into the use of the process failure mode and effects analysis (FMECA) in the automotive industry in the UK[END_REF].

The FMECA is carried out by a cross-functional and multidisciplinary team of experts (typically composed of engineers and technicians specialized in design, testing, reliability, quality, maintenance, manufacturing, safety, etc.), coordinated by a team leader. The experts must overcome conflicting situations and converge towards a shared agreement.

The most critical activity is concerned with the priority assessment of failure modes/causes, based on the Risk Priority Number (RPN), which is a composite indicator given by the product of the three dimensions of occurrence (O), severity (S), and detection (D). Each of these dimensions is determined by collective judgment, using a conventional ordinal scale from 1 to 10. The failure modes with higher RPNs are considered more critical and deserve priority for the implementation of risk mitigation actions: since the resources (time and money) available for corrective actions are (by definition) limited, it is reasonable to concentrate them where they are most needed, tolerating the minor failure modes.

The traditional method for prioritizing failure modes shows important shortcomings, extensively debated in the scientific literature [START_REF] Certa | A Dempster-Shafer Theory-based approach to the Failure Mode, Effects and Criticality Analysis (FMECA) under epistemic uncertainty: application to the propulsion system of a fishing vessel[END_REF][START_REF] Liu | Systematic failure mode and effect analysis using a hybrid multiple criteria decision-making approach[END_REF][START_REF] Das Adhikary | Multi criteria FMECA for coal-fired thermal power plants using COPRAS-G[END_REF]; including but not limited to:

• Use of arbitrary reference tables for assigning scores to the three dimensions S, O and D. • The three dimensions S, O and D are arbitrarily considered as equally important.

• Since S, O and D are evaluated using ordinal scales, their product is not a meaningful measure according to the measurement theory [START_REF] Franceschini | Designing Performance Measurement Systems: Theory and Practice of Key Performance Indicators[END_REF]. • The degree of disagreement among the team members in formulating collective judgments is not taken into account.

It is particularly challenging to assess the role of FMECA in the current globalised scenario, which is increasingly characterised by distributed manufacturing processes i.e., a form of decentralized manufacturing practiced by enterprises, using a network of geographically dispersed facilities that are supposed to be flexible, reconfigurable and coordinated through information technology. Unfortunately, decentralized production in some ways hampers the application of the traditional FMECA. Firstly, the fact that experts are numerous may increase the chances of conflicts [START_REF] Cai | A multi-stage conflict style large group emergency decision-making method[END_REF]. Secondly, only few of them generally have competence on HRC. Thirdly, there is not a great deal of experience on which to rely on.

The purpose of this paper is to revisit the traditional FMECA approach, making it reliable also when there is a substantial disagreement among experts on the potential problems and failure risks of a process. The revisited approach allows the aggregation of individual judgments by experts, through a recent aggregation technique -called ZMII -which combines the Thurstone's Law of Comparative Judgment (LCJ) and the Generalized Least Squares (GLS) method [START_REF] Thurstone | A law of comparative judgment[END_REF][START_REF] Kariya | Generalized least squares[END_REF][START_REF] Franceschini | Fusion of partial orderings for decision problems in Quality Management[END_REF].

The remainder of the paper is organized into four sections. Sect. 2 introduces a real-life case study that will accompany the explanation of the proposed approach. Sect. 3 briefly recalls the ZMII-technique. Sect. 4 illustrates the proposed methodology in detail, exemplifying its application to the above case study. Finally, Sect. 5 summarizes the original contributions of this paper, its practical implications, limitations and suggestions for future research.

Case Study

An important multinational company (anonymous for reasons of confidentiality) designs, develops, manufactures and markets seats for a number of applications, ranging from cars to aircrafts. Since the relevant assembly operations are complex and require a relatively high level of dexterity, they are largely manual. To support operators in critical manual tasks, reducing the possibility of error, collaborative robots are introduced. The case study is the assembly of a seat-frame component, which consists of fixing different flanges on a common base. Fig. 1a shows the flowchart of human and robot interaction through a Human Robot Interaction System (HRIS). In Fig. 1b a simplified part is designed for sake of laboratory tests. The most frequent collaborative task is when the robot holds a flange in position and the operator fixes it with screws; this operation is performed collaboratively, as illustrated in Fig. 1c.

The company carries out this manufacturing process in four worldwide plants located in four countries (i.e., Germany, Poland, United States and China). Since, the employed equipment is almost equivalent, it is reasonable to expect that equivalent processes are likely to be subject to the same failure modes/causes. Following this reasoning, it would be appropriate to share the experience accumulated in the various production facilities.

The above four processes are managed by twenty total engineers/technicians, hereinafter referred to as "experts". Given the great difficulty in bringing together all the experts and making them interact to reach shared decisions, the traditional FMECA approach would be extremely difficult to manage, especially with reference to activities concerning the formulation of collective judgments.

The initial activities of data collection, process analysis and determination of failure modes/causes are coordinated by a team leader, who collects information and technical indications received from other experts, processing and organizing them appropriately. The results of the initial activities are summarised in the (incomplete) FMECA table in Fig. 2, in which twelve failure mode-cause combinations have been determined (f1 to f12), which should be prioritized according to the three factors of interest. It is interesting to note that the potential (negative) effects of failure modes mainly concern interruptions/slowdowns in assembly operations, with no real safety risks. This is no longer surprising, given the relatively stringent safety standards of collaborative robots [START_REF]Robots and robotic devices -Collaborative robots[END_REF].

Collective assignments of the S, O and D scores and their aggregation through RPN will be completed using the new revisited FMECA.

ZMII-technique

The ZMII-technique can be used more generally for any large group-decision problem in which a number of judges express their individual judgments on certain objects, based on the degree of specific attributes [START_REF] Franceschini | Fusing incomplete preference rankings in design for manufacturing applications through the ZMII-technique[END_REF][START_REF]Evaluation of measurement data -Guide to the expression of uncertainty in measurement[END_REF]. Considering the case study in Sect. 2, we can identify three separate decision-making problems in which:

• the judges are the twenty experts (e1 to e20) affiliated to four production plants of the company of interest. • the objects are the failure mode-cause combinations (f1 to f12) in Fig. 2; for the sake of simplicity, these objects will be hereafter referred to as "failure modes". • the attributes are respectively S for the first problem, O for the second problem, and D for the third problem.

The ZMII-technique can be seen as a black box transforming some specific input data -i.e., judgments on n failure modes, formulated by m experts -into some specific output data -i.e., ratio scaling of the failure modes, with a relevant uncertainty estimation. Precisely, for each (i-th) failure mode, the ZMII-technique produces an estimate of (1) the (mean) ratio-scale value yi and (2) the corresponding standard deviation i y  .

A prerequisite of the ZMII-technique is that each expert formulates a ranking of the failure modes -i.e., an ordered sequence of them, with those having the highest grade of the attribute in the top positions and those having the lowest grade of the attribute in the bottom ones. E.g., considering the case study in Sect. 2, the failure modes are supposed to be ranked according to the degree of each of the dimensions of interest (i.e., S, O or D).

Apart from the regular failure modes, experts may also include two (fictitious) dummy failure modes in their rankings: i.e., one (fZ) corresponding to the absence of the attribute of interest, and one (fM) corresponding to the maximum-imaginable degree of the attribute. Referring to the case study, fZ corresponds to a fictitious failure mode of absent severity/occurrence/detection (e.g., a failure mode associated with the score S=1/O=1/D=1 [START_REF] Aiag | Automotive Industry Action Group) and VDA (Verband der Automobilindustrie)[END_REF]), while fM corresponds to a fictitious failure mode of the maximum-imaginable severity (e.g., a failure mode associated with the score S=10/O=10/D=10).

In the best cases, experts formulate complete rankings, characterised by relationships of strict dominance (e.g., "fi > fj") or indifference (e.g., "fi  fj") among the possible pairs of failure modes [START_REF] Franceschini | A new approach for evaluation of risk priorities of failure modes in FMECA[END_REF]. The formulation of these rankings may be problematic when the number of failure modes is large. To overcome this obstacle, a flexible response mode that tolerates incomplete rankings can be adopted.

Returning to the case study, each of the experts formulates his/her own three distinct (subjective) rankings of the failure modes, based on the three dimensions S, O and D; results are shown in Fig. 3. It can be noted that most of the experts have opted for the formulation of incomplete rankings, probably because they are simpler and faster. This kind of response mode may also favour data reliability since, in case of indecision, experts are not necessarily forced to provide complete and falsely precise responses [START_REF] Franceschini | A new approach for evaluation of risk priorities of failure modes in FMECA[END_REF].

Rankings indicate a significant inter-expert disagreement; e.g., while several experts place the failure mode f5 among the top positions of their S-rankings, others place it among the bottom positions. This reflects the actual difficulty of experts to converge towards a collective judgement.

The mathematical formalization of the problem relies on the postulates and simplifying assumptions of the Law of Comparative Judgment (LCJ) by Thurstone [START_REF] Thurstone | A law of comparative judgment[END_REF], who postulated the existence of a psychological continuum, i.e., an abstract and unknown unidimensional scale, in which objects are positioned depending on the degree of a certain attribute. The position of a generic i-th object (fi) is postulated to be distributed normally, in order to reflect the intrinsic expert-to-expert variability: fi ~ N(xi, i 2 ), where xi and i 2 are the unknown mean value and variance related to the degree of the attribute of that object.

Considering two generic objects, fi and fj, and having introduced further simplifying hypotheses [START_REF] Stamatis | Failure mode and effect analysis: FMECA from theory to execution[END_REF] (e.g., lack of correlation, i 2 =  2 i  , …), it can be asserted that:

pij = P[(fi -fj)> 0] = 1 -[-(xi -xj)], (1)
Extending the reasoning to all possible pairs of objects, an over-determined system of equations (similar to that in Eq. 1) can be obtained and solved by applying the Generalized Least Squares (GLS) method [START_REF] Kariya | Generalized least squares[END_REF], which allows to obtain an estimate of the mean value of the degree of the attribute of each failure mode: X = […, xi, …] T , which is expressed on an arbitrary interval scale, with a relevant dispersion estimation.

Process Expert

Ranking type Manage fZ/fM? t/b value Order t/b-objects? Dimension Ranking

1. GER e1 Complete Yes N/A N/A S (fM~f8)>f3>(f1~f9)>(f2~f12)>f4>f11>(f5~f10)>f6>(f7~fZ) O fM>f3>f8>(f4~f7~f1~f11)>(f6~f2)>f10>f12>f5>(f9~fZ) D (fM~f9~f10~f12)>f6>f7>f4>(f8~f2)>(f11~f3)>f1>f5>fZ e2 Type-t N/A 2 No S {fM||f5||f9}>{fZ||f1||f2||f3||f4||f6||f7||f8||f10||f11||f12} O {fM||f3||f11}>{fZ||f1||f2||f4||f5||f6||f7||f8||f9||f10||f12} D {fM||f6||f10}>{fZ||f1||f2||f3||f4||f5||f7||f8||f9||f11||f12} e3 Type-t&b N/A 3 No S {fM||f1||f2||f5||f8||f9}>{f3||f4||f10||f12}>{fZ||f6||f7||f11} O {fM||f2||f6||f12}>{f1||f3||f4||f7||f10||f11}>{fZ||f5||f8||f9} D {fM||f7||f10||f12}>{f1||f2||f4||f5||f6||f9}>{fZ||f3||f8||f11} e4 Type-t&b N/A 2 No S {fM||f3||f8}>{f2||f4||f6||f7||f9||f12}>{fZ||f1||f5||f10||f11} O {fM||f5||f7||f11||f12}>{f2||f3||f4||f6}>{fZ||f1||f8||f9||f10} D {fM||f6||f9}>{f1||f2||f3||f4||f5||f10||f11||f12}>{fZ||f7||f8} e5 Quasi-complete No N/A N/A S {fM||(f2~f9)}>(f8~f1)>(f5~f6~f10~f12)>(f4~f7~f3)>{fZ||f11} O {fM||(f3~f11~f12)}>f2>(f1~f6)>f10>f5>f8>{fZ||(f9~f4~f7)} D {fM||(f5~f6~f9)}>f12>f3>f11>f2>f10>f4>f8>f7>{fZ||f1} e6 Type-t N/A 3 No S {fM||f3||f8||f9}>{fZ||f1||f2||f4||f5||f6||f7||f10||f11||f12} O {fM||f3||f6||f12}>{fZ||f1||f2||f4||f5||f7||f8||f9||f10||f11} D {fM||f5||f10||f12}>{fZ||f1||f2||f3||f4||f6||f7||f8||f9||f11} e7 Type-t&b N/A 2 No S {fM||f2||f8}>{f1||f5||f7||f9||f10||f11||f12}>{fZ||f3||f4||f6} O {fM||f2||f3||f5||f12}>{f1||f8||f10||f11}>{fZ||f4||f6||f7||f9} D {fM||f5||f9}>{f3||f6||f7||f8||f10||f11||f12}>{fZ||f1||f2||f4} 2. POL e8 Type-t Yes 3 Yes S (fM~f8)>(f5~f1)>{fZ||f2||f3||f4||f6||f7||f9||f10||f11||f12} O (fM~f3)>f6>(f2~f8)>{fZ||f1||f4||f5||f7||f9||f10||f11||f12} D (fM~f9)>f10>f3>{fZ||f1||f2||f4||f5||f6||f7||f8||f11||f12} e9 Type-t&b Yes 3 Yes S (fM~f4~f8)>f3>{f1||f2||f5||f6||f9||f11}>(f7~f10~fZ~f12) O (fM~f2)>f6>f10>{f1||f3||f5||f7||f11||f12}>(f9~f4~fZ~f8) D (fM~f5~f9)>f4>{f3||f7||f8||f10||f12}>(f11~f6)>f1>(fZ~f2) e10 Type-t N/A 2 No S {fM||f5||f9}>{fZ||f1||f2||f3||f4||f6||f7||f8||f10||f11||f12} O {fM||f3||f12}>{fZ||f1||f2||f4||f5||f6||f7||f8||f9||f10||f11} D {fM||f3||f4||f12}>{fZ||f1||f2||f5||f6||f7||f8||f9||f10||f11} e11 Type-t&b N/A 3 No S {fM||f5||f8||f9||f12}>{f1||f3||f4||f6||f11}>{fZ||f2||f7||f10} O {fM||f3||f10||f11}>{f1||f4||f5||f6||f7||f12}>{fZ||f2||f8||f9} D {fM||f3||f4||f9}>{f5||f6||f7||f10||f11||f12}>{fZ||f1||f2||f8} 3. USA e12 Type-t Yes 2 Yes S (fM~f5)>f3>{fZ||f1||f2||f4||f6||f7||f8||f9||f10||f11||f12} O fM>f6>(f2~f1~f11)>{fZ||f3||f4||f5||f7||f8||f9||f10||f12} D (fM~f10~f12)>{fZ||f1||f2||f3||f4||f5||f6||f7||f8||f9||f11} e13 Type-t&b Yes 2 Yes S fM>f8>(f4~f12)>{f1||f2||f3||f5||f7||f9||f11}>(f6~f10)>fZ O fM>(f3~f6)>{f1||f2||f4||f7||f8||f11||f12}>(f9~f10~f5~fZ) D (fM~f9~f12)>{f2||f3||f4||f5||f6||f8||f10||f11}>f7>(f1~fZ) e14 Type-t N/A 3 No S {fM||f3||f5||f9}>{fZ||f1||f2||f4||f6||f7||f8||f10||f11||f12} O {fM||f3||f5||f11}>{fZ||f1||f2||f4||f6||f7||f8||f9||f10||f12} D {fM||f2||f10||f11||f12}>{fZ||f1||f3||f4||f5||f6||f7||f8||f9} e15

Proposed Methodology

The experts' rankings related to a certain dimension (S, O and D) are aggregated through the application of the ZMII-technique. For a generic (i-th) failure mode, the aggregation can be performed through the classic multiplicative model of the RPN [START_REF] Stamatis | Failure mode and effect analysis: FMECA from theory to execution[END_REF].

The uncertainty related to the RPNi values can be determined by applying delta method, also referred as law of propagation of uncertainty or error transmission formula [START_REF] Franceschini | A new approach for evaluation of risk priorities of failure modes in FMECA[END_REF]. It is thus obtained:

( ) ( ) ( ) 2 2 2 2 2 2 i i i i D i i O i i S i i RPN O S D S D O       +   +   = , (2)
where

2 i S  , 2 i O  and 2 i D
 are the variances associated with the Si, Oi and the Di values related to the i-th failure mode.

The results of the application of the proposed methodology to the case study are shown in Table 1 and ) indicate that the RPNi alone is a "myopic" indicator, since it may perform differentiations that are unfounded from a statistical point of view. For instance, while it makes sense to say that f3 is more critical than f10 or f11 (being the uncertainty band not superimposed), it can not necessarily be said that f10 deserves priority over f11. These considerations give the team a few more degrees of freedom in the choice of corrective actions, perhaps taking into account other external constraints (such as cost, technical difficulty, time required, etc..).

Finally, we note that failure causes with higher RPNi values tend to have higher dispersion. This sort of heteroschedasticity depends on the multiplicative aggregation model of S, O and D. The model could be replaced by other models (e.g. additive ones), in which appropriate weights could be introduced for weighing the three dimensions. ).

Conclusions

The paper illustrated an innovative approach for FMECA, when applied to HRC in distributed manufacturing environments. This approach has important implications that make it more suitable than the traditional FMECA for this practical context, characterised by the greater difficulty of experts in converging towards a collective decision. Among the advantages: the method does not require experts to meet physically and make collective decisions; it includes a flexible response mode; it provides an estimation of the uncertainty of the results.

Although there is no absolute reference ("gold standard") to evaluate the validity of the proposed approach with respect to the traditional one, we believe that it is superior from the conceptual point of view as it overcomes some widely debated shortcomings of the classic FMECA (e.g., it does not require the use of arbitrary reference tables for S, O and D and it does not introduce any unduly "promotion" of the judgment scales).

Among the limitations: the proposed response mode, although being flexible, represents a novelty that could create some problems, especially for more experienced users that are accustomed to the traditional procedure; like the traditional procedure, the three dimensions S, O and D were considered as equally important.

Regarding the future, we plan to replace the classic multiplicative model of the RPN with a new one that allows (1) to weigh the contributions of S, O and D and [START_REF] Antonelli | Qualification of a collaborative human-robot welding cell[END_REF] to visualize their uncertainty contribution on the resulting RPN values.

Fig. 1 . 1 -Fig. 2 .

 112 Fig. 1. Synthetic of a sub-process of collaborative Human-Robot assembly: (a) assembly diagram, (b) drawing of the components to be assembled, and (c) example of assembly task performed collaboratively.

Fig. 3 .

 3 Fig. 3. Rankings of failure modes, formulated by the experts for each of the three dimensions (S, O and D). The failure modes identified directly by the experts are marked in black, while the reconstructed parts are marked in red.

  synthetically represented in the Pareto chart of Fig. 4. The most critical failure modes are those with higher RPNi values. The relatively wide uncertainty bands (depicting the expanded-uncertainty values

Fig. 4 .

 4 Fig. 4. Pareto chart of the failure modes based on RPNi values and relevant expandeduncertainty bands (

Table 1 .

 1 Results of the analysis in terms of mean value and standard deviation of the Si, Oi, Di values and corresponding RPNi values. Mean St.dev. Mean St.dev. Mean St.dev.

	Si values	Oi values	Di values	RPNi values
	RPN Mean St.dev. i U
	f1 5.35	0.54 4.81	0.53 1.05	0.67 235.9	36.1 70.7
	f2 4.68	0.54 5.91	0.49 3.55	0.56 222.8	33.2 65.1
	f3 5.92	0.50 7.57	0.53 4.97	0.50 138.3	23.8 46.6
	f4 4.63	0.52 2.80	0.56 5.18	0.52	98.1	20.9 40.9
	f5 6.00	0.49 4.34	0.50 5.32	0.51	89.4	24.3 47.7
	f6 2.71	0.60 5.71	0.50 5.78	0.51	89.4	22.7 44.5
	f7 2.57	0.57 4.17	0.54 3.79	0.54	67.3	16.9 33.1
	f8 8.78	0.56 3.64	0.53 2.80	0.62	50.3	16.3 32.0
	f9 7.53	0.56 0.19	0.71 7.78	0.50	45.8	15.6 30.5
	f10 2.33	0.60 2.92	0.54 7.40	0.48	40.6	11.9 23.3
	f11 2.21	0.62 6.28	0.52 3.31	0.57	26.9	17.7 34.7
	f12 4.76	0.54 6.33	0.51 7.83	0.49	11.3	41.8 81.9