A Systematic PHM Approach for Anomaly Resolution: A Hybrid Neural Fuzzy System for Model Construction



Published Mar 26, 2021
Piero Bonissone Xiao Hu Raj Subbu


We analyze potential causes of anomalies, as they vary from incipient system failures to malfunctioning sensors, operating the asset in unusual regions, using inappropriate anomaly detection models, etc. For each cause, we follow the PHM cycle, creating an anomaly resolution action. Within this systematic approach, we focus on one of the most neglected causes for anomalies: the inadequate accuracy of anomaly detection models. We describe a hybrid approach based on a fuzzy supervisory system and an ensemble of locally trained auto associative neural networks (AANN’s). The supervisory system will manage the transition among local AANN’s during operating regime changes. This approach is illustrated with experiments with a simulated aircraft engine.

How to Cite

Bonissone, P. ., Hu, X. ., & Subbu, R. . (2021). A Systematic PHM Approach for Anomaly Resolution: A Hybrid Neural Fuzzy System for Model Construction. Annual Conference of the PHM Society, 1(1). Retrieved from https://papers.phmsociety.org/index.php/phmconf/article/view/1690
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anomaly detection, neural network

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