Data-Driven Open-Set Fault Classification of Residual Data Using Bayesian Filtering
Daniel Jung
Abstract
Data-driven fault classification in industrial applications is complicated by unknown fault classes and limited training data. In addition, different faults can have similar effects on sensor outputs resulting in fault classification ambiguities, i.e., multiple fault hypotheses can explain the data. One solution is to identify and rank all plausible fault classes that give useful information, for example, at a workshop when performing troubleshooting. A probabilistic fault classification algorithm is proposed for residual data classification combining the Weibull-calibrated one-class support vector machines for fault class modeling and Bayesian filtering for time-series analysis. The fault classifier ranks different fault classes and can identify sequences from unknown fault realizations, i.e., faults not represented in training data. Real residual data computed from sensor data and model analysis of an internal combustion engine are used as a case study illustrating the usefulness of the proposed method.