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Unsupervised deviation detection by GMM - A simulation study

Svensson, Magnus, Rögnvaldsson, Thorsteinn, Byttner, Stefan, West, Martin, Andersson, Björn
2011

Konferensbidrag (Refereegranskat)

Abstract:

A new approach to improve fault detection of electrical machines is proposed. The increased usage of electrical machines and the higher demands on their availability requires new approaches to fault detection. In this paper we demonstrate that it is possible to detect a certain fault on a PMSM (Permanent Magnet Synchronous Machine) by using multiple similar motors, or a single motor, to build a norm of expected behavior by monitoring signal relations. This means that the machine is monitored in an unsupervised way. Four levels of an increased temperature in the rotor magnets have been investigated. The results are based on simulations and the signals used (for relation measurements) are available in a real motor installation. The method shows promising results in detecting two of the temperature faults. © 2011 IEEE.

Nyckelord: Data mining; fault detection; machine learning; mechatronics; PMSM

Citera: Svensson, Magnus, Rögnvaldsson, Thorsteinn, Byttner, Stefan, West, Martin & Andersson, Björn, Unsupervised deviation detection by GMM - A simulation study, SDEMPED 2011 : 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives : September 5-8, 2011, Bologna, Italy., 2011