Demodulation analysis based on adaptive local iterative filtering for bearing fault diagnosis

The vibration signals from defective rolling bearings are multi-component amplitude modulation (AM)-frequency modulation (FM) signals. Traditional envelope analysis method is based on a filter. The centre frequency and bandwidth of the filter are set according to experience. So the filter method will induce a demodulation error. This research proposes a rolling bearing fault diagnosis method based on adaptive local iterative filtering (ALIF) and envelope spectrum. The ALIF method is a new method for the analysis of non-stationary signals. It uses an iterative filters strategy together with an adaptive, data-driven filter length selection to achieve the necessary decomposition. Smooth filters with compact support from the solutions of the Fokker-Planck equations are used within the ALIF method. The ALIF method offers good performance in obtaining more accurate components of non-stationary signals and in suppressing mode mixing. The ALIF method can decompose a multi-component AM-FM signal into a number of stationary components. The envelope demodulation method is used to analyse those components containing fault information which, in turn, can reveal bearing fault features. The vibration signals from a rolling bearing with an outer race fault and an inner race fault are used to verify the proposed method. The results show that this method can effectively extract bearing fault features.