Industrial drive fault diagnosis through vibration analysis using wavelet transform

Abstract

The fault diagnosis of electrical machines is significant to reduce the costs of maintenance through early detection of faults, which could be expensive to service. The idea of the research is that the advanced signal processing techniques called wavelet transform is exercised to haul out the faults from the vibration and current signal and a spectral component is being received to diagnose the condition of the machine. The existing system used wavelet transforms for the analysis of the stator faults and rotor faults are taken as a case study to prove the wavelet techniques for fault diagnosis. The proposed investigation of vibration analysis for the induction machines is done through frequency pattern using the decomposition of wavelet packet. The wavelet coefficients for the vibration have been extracted over a wide range of signals and the analysis could be percolated on the frequency domain through HAAR wavelet. In this paper, healthy and unhealthy motor with two broken ball bearings are used to estimate the vibration and current spectrum. Experimentally observed vibration and current signals are transformed into power spectral density through approximate and detailed coefficients with the help of MATLAB tools and the steady state rotor frequency was used to introduce a new frequency pattern for fault diagnosis.