Especially common in discrete manufacturing, timed event systems often require a high degree of synchronization for healthy operation. Discrete event system methods have been used as mathematical tools to detect known faults, but do not scale well for problems with extensive variability in the normal class. A hybridized discrete event and data-driven method is suggested to supplement fault diagnosis in the case where failure patterns are not known in advance. A unique fault diagnosis framework consisting of signal data from programmable logic controllers, a Timed Petri Net of the normal process behavior, and machine learning algorithms is presented to improve fault diagnosis of timed event systems. Various supervised and unsupervised machine learning algorithms are explored as the methodology is implemented to a case study in semiconductor manufacturing. State-of-the-art classifiers such as artificial neural networks, support vector machines, and random forests are implemented and compared for handling multi-fault diagnosis using programmable logic controller signal data. For unsupervised learning, classifiers based on principal component analysis utilizing major and minor principal components are compared for anomaly detection. The rule-based extreme random forest classifier achieves the highest validation accuracy of 98% for multi-fault classification. Likewise, the unsupervised learning approach shows similar success, yielding anomaly detection rates of 98% with false alarms under 3%. The industrial feasibility of this method is notable, with the results achieved with a training set 99% smaller than the supervised learning classifiers.

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