Tri-axial Vibration Based Collective Feature Analysis for Decent Fault Classification of VFD Fed Induction Motor

Abstract This paper has explored the multi-class fault diagnosis of variable frequency drive (VFD) fed induction motor over a wide operating range (5-50 Hz) using tri-axial vibration signals. The comprehensive study has converged towards an optimum solution of discrete wavelet transform–inverse discrete wavelet transform based feature extraction algorithm using Daubechies2 mother wavelet. In the present investigation, the data-driven approach, supported by experimental verification, has established the horizontal frame vibration to be the most fault informative followed by vertical and axial. However, a combination of typical tri-axial collective features with 36.4% of horizontal (4) , 36.4% of vertical (4) & 27.2% of axial (3) features, picked up by RELIEFF based feature selection technique, have demonstrated very satisfactory performances with statistically independent experimental data set. The developed standalone fault diagnosis model over the entire VFD range, outperforms over the contemporary methodologies, even employing shallow classifier like multilayer perceptron. The collective tri axial feature with the data mining technique not only unveiled buried information in the area of vibration signal based motor fault diagnosis, at the same time reduces the computational burden to a great extent. Thus making the proposed algorithm robust and hardware friendly, suitable for low-cost instrumentation.