EveSyncIAI: Event Synchronization Industrial Augmented Intelligence for Fault Diagnosis

Abstract

The fault diagnosis of complex timed discrete event systems remains a relevant and challenging problem in industry. As systems degrade, the synchronization required for normal operation associated with timed event sequences deteriorate. However, existing discrete event approaches cannot handle complexity well as there may be potentially hundreds of event sequences that still result in normal system behavior, and they do not scale well due to the state explosion problem. To circumvent these issues, a new data-driven augmented intelligence framework called EveSyncIAI is proposed that uses a Timed Petri Net of the normal process to enable selective extraction of discriminating time delay features, which are then used alongside machine learning for fault diagnosis. The developed methodology is applied to a case study of an operational discrete manufacturing system in the semiconductor industry. Multiclass machine learning approaches as well as binary anomaly detection algorithms with varying levels of supervision are directly compared with and without EveSyncIAI-extracted features. With precision, recall, and F1-score values above 0.90 being achieved for multi-fault classification and AUC scores exceeding 0.98 for semi-supervised and unsupervised anomaly detection approaches, the framework presented is promising for the fault diagnosis of timed sequence-sensitive systems.