Anomaly Detection Using Model Generation for Event-Based Systems Without a Preexisting Formal Model

Detecting and debugging faults more efficiently can significantly improve the performance of systems, and a first step toward fault detection is anomaly detection. A new anomaly detection solution is proposed in this paper for event-based systems that consist of processes that interact through shared resources and that do not have a preexisting formal discrete event system model. This solution generates models of the system, assesses the models´ performance in detecting faults, and then uses the models and their performance to detect anomalies in new event streams. A new resource-based Petri net formalism is introduced to model these types of systems. The model generation uses an algorithm based on workflow mining to generate resource-based models. The proposed solution is demonstrated on two manufacturing cell examples.