Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals

Abstract

Since spalling in ball bearings seriously degrades operating conditions in machine systems, in-process methods for predicting spalling are needed to keep the systems running properly. In this paper, the discrete wavelet transform (DWT) is applied to vibration signals to predict the occurrence of spalling in ball bearings. The DWT yields information on both the time and frequency characteristics of the input signals, and is particularly helpful in detecting subtle time localized changes. Experiments are conducted by analyzing the vibration signals of dented thrust ball bearing. A comparison of the signals' DWT coefficients at various resolutions yields detailed information on the transient vibration phenomena which occurs when a ball passes on the pre-spalling of the raceway. Prediction of spalling is possible through inspection of the trends of the wavelet coefficient values.