Rotating machinery fault diagnosis based on maximum correlation kurtosis deconvolution and reassigned wavelet scalogram
Aiming at the problem that the rotating machinery composite faults signal is difficult to be extracted and segmented from strong noise background，a fault diagnosis method for rotating machinery based on maximum correlated kurtosis deconvolution (MCKD) and reassigned wavelet scalogram was proposed. The noise in the rotating machinery vibration signal would reduced readability of its time-frequency representation，so the noise should be reduced by using MCKD，and the fault components were separated from the vibration signal, and then envelopes were obtained by Hilbert transform, finally envelopes were analyzed with the reassigned wavelet scalogram and the composite faults of rotating machinery were diagnosed according to the periods of impulse components in the scalogram, algorithm simulation and application examples validate the method.
Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance，China Three Gorges University，Yichang，Hubei，443002，China
Abstract：Aiming at the problem that the rotating machinery composite faults signal is difficult to be extracted and segmented from strong noise background，a fault diagnosis method for rotating machinery based on maximum correlated kurtosis deconvolution (MCKD) and reassigned wavelet scalogram was proposed. The noise in the rotating machinery vibration signal would reduced readability of its time-frequency representation，so the noise should be reduced by using MCKD，and the fault components were separated from the vibration signal, and then envelopes were obtained by Hilbert transform, finally envelopes were analyzed with the reassigned wavelet scalogram and the composite faults of rotating machinery were diagnosed according to the periods of impulse components in the scalogram, algorithm simulation and application examples validate the method.
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