Detection of Faults in Induction Motors Using Embedded Zero-tree Wavelet Analysis of Stator Current Signals

This paper discusses the detection of commonly occurring electrical and mechanical faults in three-phase induction motor. Besides, it explicates the underlying tenet, causes and consequences of frequently occurring faults in induction motors. The paper attempts to discuss different types of induction motor faults, their causes and detection techniques. It is found that the detection techniques which evaluate the dynamic behavior of the signal (such as Wavelet Transform analysis) are best suited for the purpose. The Embedded Zero-tree Wavelet (EZW) technique relieves from the dreary handling of the bulk data by reducing the dimensionality of the data. The competence of the EZW algorithm is expounded through implementation of the scheme on the stator current signals derived during electrical and mechanical faults in Induction motors. The experimentation to corroborate the scheme has been performed on a 3-Φ, 1.5 kW, 4P, 1440 RPM, ABB squirrel cage motor. The dSPACE DS 1104 DSP control card (based on TMS 320F240 DSP), dSPACE controldesk software and the MATLAB software have been used as the data acquisition tool and programming platform.