Title :
Anomaly Detection Using Model Generation for Event-Based Systems Without a Preexisting Formal Model
Author :
Allen, Lindsay V. ; Tilbury, Dawn M.
Author_Institution :
Dept. of Electr. Eng.: Syst., Univ. of Michigan, Ann Arbor, MI, USA
fDate :
5/1/2012 12:00:00 AM
Abstract :
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.
Keywords :
Petri nets; discrete event systems; fault diagnosis; anomaly detection solution; fault debugging; fault detection; manufacturing cell example; preexisting formal discrete event-based system model generation; resource sharing; resource-based Petri net formalism model; workflow mining; Availability; Fault detection; Machining; Manufacturing; Mathematical model; Milling machines; Robots; Discrete-event systems; Petri nets; fault diagnosis; modeling;
Journal_Title :
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
DOI :
10.1109/TSMCA.2011.2170418