Autocorrelation function-based technique for stator turn-fault detection of induction motor

作者：Ghanbari Teymoor^*

来源：IET Science Measurement & Technology, 2016, 10(2): 100-110.

DOI：10.1049/iet-smt.2015.0118

Stator current-based monitoring of induction motors (IMs) named motor current signature analysis (MCSA) has significant economic and technical advantages over other proposed techniques for fault diagnosis in different parts of the motor. Stator, rotor, and bearing faults as more probable fault types of IMs can be detected by signature analysis of the stator current. One of the attractive MCSA approaches is based on fault signatures in the stator currents envelope. This study deals with a simple technique for detection of IMs stator turn-faults. The proposed approach is based on analysis of modal voltage (MV) and modal current (MC) of the stator. Envelope of the MC is derived and its autocorrelations are considered as the fault detection criterion. Since variation of autocorrelation of MC envelope (AMCE) is negligible in normal condition of IMs and considerable in stator turn-fault, this criterion can accurately detect the fault condition. However, some voltage quality problems can also have similar impact on the AMCE variations. Therefore, envelope of the MV is filtered and checked as supplementary criterion in order to prevent malfunction of the method. Simulation and experimental results confirm high degree of accuracy of the proposed method.

• 出版日期2016-3