A Syntactic Pattern Recognition Based Approach to Online Anomaly Detection and Identification on Electric Motors

Coskun K., Kumralbas Z., Cavus H., TÜMER M. B.

44th DAGM German Conference on Pattern Recognition (DAGM GCPR), Konstanz, Germany, 27 - 30 September 2022, vol.13485, pp.116-132, (Full Text) identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 13485
  • Doi Number: 10.1007/978-3-031-16788-1_8
  • City: Konstanz
  • Country: Germany
  • Page Numbers: pp.116-132
  • Keywords: Syntactic Pattern Recognition, Variable Order Markov Models, Probabilistic Suffix Trees, Anomaly detection, Diagnosis, INTELLIGENT FAULT-DIAGNOSIS, INDUCTION-MOTORS, NETWORK, METHODOLOGY, VIBRATION, TREE
  • Marmara University Affiliated: Yes

Abstract

Online anomaly detection and identification is a major task of many Industry 4.0 applications. Electric motors, being one of the most crucial parts of many products, are subjected to end-of-line tests to pick up faulty ones before being mounted to other devices. With this study, we propose a Syntactic Pattern Recognition based approach to online anomaly detection and identification on electric motors. Utilizing Variable Order Markov Models and Probabilistic Suffix Trees, we apply both unsupervised and supervised approaches to cluster motor conditions and diagnose them. Besides being explainable, the diagnosis method we propose is completely online and suitable for parallel computing, which makes it a favorable method to use synchronously with a physical test system. We evaluate the proposed method on a benchmark dataset and on a case study, which is being worked on within the scope of a European Union funded research project on reliability.