Diagnosis of inverter faults in PMSM DTC drive using time-series data mining technique

  • Authors:

  • Dan Sun;Jun Meng;Zongyuan He

  • Affiliations:

  • College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang, China;College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang, China;College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang, China

  • Venue:
  • ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
  • Year:
  • 2006

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Abstract

This paper investigates a Time-Series Data Mining (TSDM) Technique based fault diagnostic method for short-switch and open-phase faults in a standard 6-switch inverter fed permanent magnet synchronous motor (PMSM) direct torque control (DTC) drive system. For diagnosing the operating condition of an inverter, the reconstructed phase space (RPS) theory is applied to obtain the special feature consisting in the trajectories of phase currents for healthy and faulty operating conditions. The fuzzy C-mean (FCM) algorithm is used to build a fuzzy membership function, an FCM based ANFIS (FCM-ANFIS) is designed to classify different fault patterns. The proposed method has been studied by simulation using MATLAB; which proves that different operating conditions of PMSM DTC drive can be discovered clearly without background knowledge.