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).

Tool failure may result in losses in surface finish and dimensional accuracy of a finished part, or possible damage to the work piece and machine. This paper presents a Fuzzy Neural Network (FNN) which is designed and developed for machinery prognostic monitoring. The FNN is basically a multi-layered fuzzy-rule-based neural network which integrates a fuzzy logic inference into a neural network structure. The fuzzy rules help to speed up the learning process of the complex conventional neural network structure and improve the accuracy in prediction and rate of convergence.