Abstract
Digital technologies are becoming more pervasive and industrial companies are exploiting them to enhance the potentialities related to Prognostics and Health Management (PHM). Indeed, PHM allows to evaluate the health state of the physical assets as well as to predict their future behaviour. To be effective in developing PHM programs, the most critical assets should be identified so to direct modelling efforts. Several techniques could be adopted to evaluate asset criticality; in industrial practice, criticality analysis is amongst the most utilised. Despite the advancement of artificial intelligence for data analysis and predictions, the criticality analysis, which is built upon both quantitative and qualitative data, has not been improved accordingly. It is the goal of this work to propose an ontological formalisation of a multi-attribute criticality analysis in order to i) fix the semantics behind the terms involved in the analysis, ii) standardize and uniform the way criticality analysis is performed, and iii) take advantage of the reasoning capabilities to automatically evaluate asset criticality and associate a suitable maintenance strategy. The developed ontology, called MOCA, is tested in a food company featuring a global footprint. The application shows that MOCA can accomplish the prefixed goals; specifically, high priority assets towards which direct PHM programs are identified. In the long run, ontologies could serve as a unique knowledge base that integrate multiple data and information across facilities in a consistent way. As such, they will enable advanced analytics to take place, allowing to move towards cognitive Cyber Physical Systems that enhance business performance for companies spread worldwide.
Highlights
Digital technologies are enabling Prognostics and Health Management (PHM) to become a cornerstone for companies willing to have insights on their shopfloor status [1]
The proposed ontology does not outperform the numerical results obtained through already available multi-attribute criticality analysis methods, given that the underlying operations are the same, whereas MOCA is focused on the semantic formalisation and definition of relevant concepts and relationships so to establish a common background and semantic alignment between stakeholders
6 Conclusions This research work investigates how to model the multiattribute criticality analysis through ontologies. This is driven by the relevance that this kind of analysis has in current industrial context since PHM program must be directed to those assets that have high criticality
Summary
Digital technologies are enabling Prognostics and Health Management (PHM) to become a cornerstone for companies willing to have insights on their shopfloor status [1]. The realised ontological model is tested in a global manufacturer in the food sector to prioritise the assets in a new plant in order to plan maintenance strategies in advance in its BoL and, to better direct PHM-related investments This required to identify and establish a common criticality analysis methodology to be adopted, as standard approach, in other plants to make the evaluation uniform. Ontologies, models related to symbolic AI, are used in the scientific literature and industry to set up common and agreed-upon terminologies between stakeholders Their reasoning capabilities offer support in the automatic allocation of maintenance strategies to proper assets, and its related components, combining information coming from diagnostics, for which ontologies are already established, powerful state-of-art means [28].
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