Abstract
Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and a challenge is how to accurately separate the inner and outer race fault features from noisy compound faults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Q-factors and improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, the compound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonance components of the signal (compound fault impact component and small amount of noise) are obtained, but it can only highlight the impact of compound faults, and failed to separate the inner and outer race compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection of parameters (the shift order M and the filter length L) based on the iterative calculation method with the Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filtered and de-noised by the proposed method, the inner and outer race fault signals are obtained respectively. The fault characteristic frequency is consistent with the theoretical calculation value. The results show that the proposed method can efficiently separate the mixed fault information and avoid the mutual interference between the components of the compound fault.
Highlights
Rolling bearing is one of the most widely used parts in rotating machinery
The maximum correlated kurtosis deconvolution (MCKD) technique is based on selecting a Finite Impulse Response filter (FIR filter) to maximize the correlated kurtosis (CK), which takes advantage of the periodicity of the faults and is used to highlight the continuous pulse covered by the strong noise in the signal
This study presents a compound fault diagnosis method for rolling bearings based on a parallel dual-Q-factor and the improved maximum correlated kurtosis deconvolution (IMCKD)
Summary
Rolling bearing is one of the most widely used parts in rotating machinery. The operational status of the rolling element bearing often directly affects the performance of the whole machine. Reference [27] proposed a method of combining wavelet analysis with blind source separation for roller bearing compound faults separation that needs multiple signal channels to analyze. McDonald et al [28] proposed a novel method named maximum correlated kurtosis deconvolution (MCKD), which is an effective tool for separating out the periodic impulse fault components from the vibration signal in circumstances of intense background noise. The proposed RSSD method can be applied to extract multiple impact components in compound fault diagnosis Another important problem is how to set the input parameters appropriately for the best performance, which contains the filter length L, shift order M and the deconvolution period T. The rigorous requirements for the parameters limit the application of MCKD To solve this problem, the improved maximum correlation kurtosis deconvolution (IMCKD) is proposed in this paper.
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