Investigations on mother wavelet selection for health assessment of lathe bearings

Abstract

Vibration in machine tools beyond safe limits is a serious concern which affects the performance of the machine tools. For predicting the degradation in performance, periodic measurement of vibration of machine tools is an important step. For dynamic signal analysis, wavelet transform has been increasingly applied for system health monitoring. One of the key steps in processing a signal with wavelet transform is a selection of appropriate mother wavelet. Different quantitative criteria being used in earlier studies for selecting mother wavelets are maximum energy, minimum Shannon entropy, and their ratio. But there is no unique agreement over mother wavelet with different wavelet selection criteria. This study endeavors to use benchmarks in signal denoising such as Peak signal to noise ratio (PSNR), Mean squared error (MSE), and Max error as wavelet selection criteria in addition to maximum energy and minimum Shannon entropy for selecting appropriate mother wavelet. The new methodology is based on assigning weights to each selection criteria based on analytic hierarchy process (AHP) depending on the nature of vibration data and then finding the overall ranking of each wavelet. Mother wavelets from Daubechies, Symlet, Coiflet, and Bior families are investigated in this study. The best mother wavelets proposed by the weighting schemes are further used for processing vibration signals using a multiresolution analysis. This methodology is successfully implemented for assessing health of bearings of critical subsystem of a lathe machine tool. The new wavelet selection strategy may be implemented with equal success for health assessment of a broad range of machine tools such as CNC lathes, milling machines, machining centers, and other delicate machine tools.