Adaptive maximum correlated kurtosis deconvolution method and its application on incipient fault diagnosis of bearing

The fault feature signal of rolling bearing is very weak and affected by environmental noise seriously in early failure period, so it is difficult to extract the fault feature. In order to solve this problem, an incipient fault diagnosis method for rolling bearing based on adaptive maximum correlated kurtosis deconvolution was proposed. Particle swarm optimization algorithm with excellent optimization characteristic was used to search for the influencing parameters of maximum correlated kurtosis deconvolution algorithm in order to achieve the best deconvolution result adaptively, then the impact characteristic of fault signal could be enhanced after processed by maximum correlated kurtosis deconvolution algorithm with optimized parameters. The envelope demodulation method was used to analyze the deconvolution signal further and the fault type could be judged by analyzing the obvious frequency components of the envelope spectrum. Analysis results of simulated and measured signal prove this method could extract the weak feature frequency information of incipient fault of rolling bearing effectively. ©2015 Chin. Soc. for Elec. Eng.