Abstract
The study focuses on health monitoring applications performing Fault Identification (FI) and Maintenance Indication (MI). In industry, third-party algorithms based on artificial intelligence could be supplied in closed-source. For this reason, they may lack of customization, or retraining, that leads to a possible poor performance. The work proposes a system called Fuzzy Set Aggregator (FSA) receiving in input unstable classifier predictions over time and provides a stable MI signal with a reasonable degree of anticipation. Uncertainty quantification and management are embedded in the aggregation process by using the type-2 fuzzy set theory. Four system variants are tested through simulation with data extracted from a real-case study. Two FSA variants were able to anticipate the expected reference values, providing valuable signals to plan maintenance at a reduced cost, and showing a performance improvement when the uncertainty due to expert opinion is greater.
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The authors wish to thank Carel Industries SpA for the domain-knowledge support associated to the aging of the monitored system.
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Bodo, R., Bertocco, M., Bianchi, A. (2022). Type-2 Fuzzy Set Aggregation and Health Status Mining from Condition-Monitoring Applications. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 216. Springer, Singapore. https://doi.org/10.1007/978-981-16-1781-2_87
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