Study on Application of CYCBD based on PSO in Fault Feature Extraction of Rolling Bearing - Yutao Liu; Huer Sun (Jixie chuandong)
The blind deconvolution based on cyclostationarity maximization （CYCBD） is applied to solve the problem that the periodic transient impacts is not obvious at the initial stage of rolling bearing failure under background noise. The noise reduction effect of CYCBD around the filter length and cycle frequency， particle swarm optimization algorithm （PSO） is applied to intelligently optimize CYCBD. Determine the optimal parameters to solve the instability of CYCBD. Firstly， PSO is used to optimize the filter length and cycle frequency in CYCBD to enhance the periodic impacts component. Then， the optimal solution of filter length and cycle frequency is iteratively found by using crest factor of envelope spectrum（EC） as the objective function of PSO. Finally， by applying the optimal solution to CYCBD and conducting envelope demodulation analysis on the enhanced signals， the fault characteristic frequency of bearing signals can be accurately obtained. Through the analysis of simulation signals and experimental data， it is shown that the method can effectively enhance the periodic transient impacts characteristics of vibration signals and has advantages in the early fault feature extraction of rolling bearings.