Compound Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by MCDK

Wan, Shuting; Zhang, Xiong; Dou, Longjiang

doi:10.1155/2018/6513045

Cited by 16 publications

(15 citation statements)

References 15 publications

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“…The number of iterations of MED and MCKD is both set to 30. Compared with the method in the literature [27], the method has good adaptability. According to the characteristic of the noisy signal, the decomposition level of FSK is 4.…”

Section: Simulations and Comparisonsmentioning

confidence: 99%

“…Mcdonald et al [26] proposed MCKD, which can extract a series of fault impacts from the vibration signal. Wan et al [27] calculated the period of MCKD according to the estimated characteristic frequency and then extracted the early fault features contained in the filtered signal by FSK. The period and filter size of MCKD need to be set in advance, so the method lacks adaptability.…”

Section: Introductionmentioning

confidence: 99%

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A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

Zhou

et al. 2019

Mathematical Problems in Engineering

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The nonstationary components and noises contained in the bearing vibration signal make it particularly difficult to extract fault features, and minimum entropy deconvolution (MED), maximum correlated kurtosis deconvolution (MCKD), and fast spectral kurtosis (FSK) cannot achieve satisfactory results. However, the filter size and period range of multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) need to be set in advance, so it is difficult to achieve satisfactory filtering results. Aiming at these problems, a parameter adaptive MOMEDA feature extraction method based on grasshopper optimization algorithm (GOA) is proposed. Firstly, the multipoint kurtosis (MKurt) of MOMEDA filtered signal is used as the optimization objective, and the optimal filter size and periodic initial value which matched with the vibration signal can be determined adaptively through multiple iterations of GOA. Secondly, the periodic impact contained in the vibration signal is extracted by the optimized MOMEDA, and the fault features in the impact signal are extracted by Hilbert envelope demodulation. Finally, the simulation signal and bearing signal are processed by the proposed approach. The results show that the introduction of GOA not only solves the problem of parameter selection in MOMEDA, but also achieves better performance compared with other optimization methods. Meanwhile, the feasibility and superiority of the approach are fully proved by comparing it with the three methods MED, MCKD, and FSK after parameter optimization. Therefore, this approach provides a novel way and solution for fault diagnosis of the rolling bearing, gear, and shaft.

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Section: Simulations and Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

Zhou

et al. 2019

Mathematical Problems in Engineering

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“…The compound fault signal was simulated by the simulation signal of the inner race and outer race. The outer race fault signal simulation model was defined as in the following equation [46]:…”

Section: The Construction Of Compound Fault Simulation Signalmentioning

confidence: 99%

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Cui

Yang

et al. 2019

Applied Sciences

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Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and achallenge is how to accurately separate the inner and outer race fault features from noisy compoundfaults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Qfactorsand improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, thecompound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonancecomponents 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 outerrace compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection ofparameters (the shift order M and the filter length L) based on the iterative calculation method withthe Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filteredand 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 showthat the proposed method can efficiently separate the mixed fault information and avoid the mutualinterference between the components of the compound fault.

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“…Abbas founded the resonance band of the defective rolling bearing through SK and achieved the rolling bearing fault identification [20]. Wan applied SK to select the resonance frequency band and extract the fault feature using the envelope demodulation method [21]. After the above analysis, we can find that the periodic impact component characterizing the rolling bearing fault feature is not obviously directly obtained by the TQWT, has a lot of interferences, and is influenced by the selected Q-factor.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Feature Extraction Based on Adaptive Tunable Q-Factor Wavelet Transform and Spectral Kurtosis

Zhao

Chen

2020

Shock and Vibration

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The fault feature of the rolling bearing is difficult to extract when weak fault occurs and interference exists. The tunable Q-factor wavelet transform (TQWT) can effectively extract the weak fault characteristic of the rolling bearing, but the manual selection of the Q-factor affects the decomposition result and only using TQWT presents interference. Aiming at the above problems, an adaptive tunable Q-factor wavelet transform (ATQWT) and spectral kurtosis (SK) method is proposed in this paper. Firstly, the method applies particle swarm optimization (PSO) to seek the optimized Q-factor for avoiding manual selection, which uses the kurtosis value of the transient impact component as the particle fitness function. The rolling bearing fault signal is decomposed into continuous oscillation component and transient impact component containing fault feature by the optimized Q-factor. Then, due to the presence of interference in the decomposition result of ATQWT, the SK analysis of the transient impact component is used to determine the frequency band of periodic impact component characterizing fault feature by fast kurtogram. Finally, the band-pass filter established through the above frequency band is employed to filter the interference in the transient impact component. Simulation and experimental results indicate that the ATQWT can highlight the periodic impact component characterizing rolling bearing fault feature, and the SK can filter interference in the transient impact component, which improves feature extraction effect and has great significance to enhance fault diagnosis accuracy of the rolling bearing. Compared with EEMD-TQWT and TQWT-SK, the fault feature extracted by the proposed method is prominent and effective.

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Compound Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by MCDK

Cited by 16 publications

References 15 publications

A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Rolling Bearing Fault Feature Extraction Based on Adaptive Tunable Q-Factor Wavelet Transform and Spectral Kurtosis

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