When rolling bearings are in the state of multiple faults, the envelope spectrum diagnosis effect will be poor due to the mutual interference between various faults responses. The maximum correlated kurtosis deconvolution (MCKD) is an effective tool for enhancing periodic pulses. However, the filter length parameter and the shift number of MCKD need to be set manually, and the bearing operation under complex conditions wants higher requirements on parameters. For this situation, a multiple faults diagnosis method for rolling bearings based on adaptive maximum correlated kurtosis deconvolution was proposed. In the method, the spectral correlation kurtosis value of the envelope spectrum of deconvolution signal was used as an objective function, applying the artificial fish swarm algorithm to get the optimal parameters of MCKD self-adaptively. Then the fault signal was processed by a maximum correlated kurtosis deconvolution algorithm with optimized parameters. The results of multi-faults simulations and bearing inner ring multiple faults experiments prove that the method can extract fault features effectively and achieve the diagnosis of multiple faults of rolling bearings accurately.
HU Aijun, ZHAO Jun.
Diagnosis of multiple faults in rolling bearings based on adaptive maximum correlated kurtosis deconvolution[J]. Journal of Vibration and Shock, 2019, 38(22): 171-177
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