Bearing fault vibration diagnosis using frequency domain semi-blind extraction method

It is usually not easy to extract fault features from the vibration signals directly, since the complexity of the mechanical structure and the serious background interference in industry testing site. In order to deal with these kinds of monitoring problems, a mechanical failure diagnosis method based on reference signal frequency domain semi-blind extraction is proposed. In this method, dynamic particle swarm algorithm is used to construct improved multi-scale morphological filters which applicable to mechanical failure in order to weaken the background noises; thus reference signal unit semi-blind extraction algorithm is applied to do complex components blind separation band by band, coupled improved KL-distance of complex independent components are employed as distance measure to resolve the permutation; finally the estimated signal could be extracted and analysed by envelope spectrum method. Comparing to the time-domain blind deconvolution algorithm based on fuzzy clustering, it has several advantages such as more effectively and more accurately. Results from rolling bearing fault diagnosis experiment validate the feasibility and effectiveness of proposed method.