Detection for Incipient Damages of Wind Turbine Rolling Bearing Based on VMD-AMCKD Method

Fault diagnosis of rolling bearings is not a trivial task because fault-induced periodic transient impulses are always submerged in environmental noise as well as large accidental impulses and attenuated by transmission path. In most hybrid diagnostic methods available for rolling bearings, the problems lie in twofolds. First, most optimization indices used in the individual signal processing stage do not take the periodical characteristic of fault transient impulses into consideration. Second, the individual stages make use of different optimization indices resulting in inconsistent optimization directions and possibly an unsatisfied diagnosis. To solve these problems, a multistage fault feature extraction method of consistent optimization for rolling bearings based on correlated kurtosis (CK) is proposed where maximum correlated kurtosis deconvolution (MCKD) is employed to attenuate the influence of transmission path followed by tunable Q factor wavelet transform (TQWT) to further enhance fault features by decomposing the preprocessed signals into multiple subbands under different Q values. The major contribution of the proposed approach is to consistently use CK as an optimization index of both MCKD and TQWT. The subband signal with the maximum CK which is an index being able to measure the periodical transient impulses in vibration signal yields an envelope spectrum, from which fault diagnosis is implemented. Simulated and experimental signals verified the effectiveness and advantages of the proposed method.

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“…e calculation is terminated as the iteration error is smaller than the threshold value. Otherwise, repeat the process from Step 3 [37,38].…”

Section: Maximum Correlated Kurtosis Deconvolutionmentioning

confidence: 99%

Multistage Fault Feature Extraction of Consistent Optimization for Rolling Bearings Based on Correlated Kurtosis

Long

Cai

Xiong

et al. 2020

Shock and Vibration

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“…Cheng et al [44] proposed the optimal minimum entropy deconvolution adjusted and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) for enhancing the impulse-like component in the fault signal. Zhang et al [45] proposed a new detected method combining complementary advantages of variational mode decomposition (VMD) and adaptive maximum correlated kurtosis deconvolution, named VMD-AMCKD. Wang et al [46] proposed multipoint optimal minimum entropy deconvolution adjusted to the fault diagnosis of gearbox.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Based on EWT With Scale Space Threshold and Improved MCKD for Fault Diagnosis

Guo²,

Chen

2021

IEEE Access

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Aiming at the problem of feature extraction of non-stationary, non-linear and weak fault signals, a new feature extraction method based on empirical wavelet transform (EWT) with scale space threshold (STEWT) and improved maximum correlation kurtosis deconvolution (MCKD) with power spectral entropy and grid search (PGMCKD), namely STEWT-PGMCKD is proposed for rolling bearing faults in this paper. In the proposed STEWT-PGMCKD method, the scale space threshold method is designed to solve the problems of falling into local extremum and mode over decomposition caused by the local-max-min band decomposition method of EWT, which is used to decompose the frequency band of signal, and the correlation analysis is carried out between the decomposed modal components and the original signal to retain the modal components with high correlation. Then an adaptive MCKD based on power spectral entropy is proposed to solve the problem that the signal processing effect of MCKD is affected by filter size L and deconvolution period T . Nextly, the parameters of the MCKD are optimized by grid search method. Finally, the power spectrum analysis of the enhanced signal is carried out to realize the feature extraction and fault diagnosis. The experiment results show that the proposed STEWT-PGMCKD method can effectively extract the weak fault information and accurately realize the fault diagnosis for rolling bearings.

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“…A large number of scholars have studied this method. In recent years, for example, Zhang J et al [21] proposed a new fault detection method for detecting rolling bearings of wind turbines called VMD-AMCKD. This method combines complementary advantages of VMD and adaptive maximum correlated kurtosis deconvolution (AMCKD).…”

Section: Introductionmentioning

confidence: 99%

Novel Adaptive Sparse-Spike Deconvolution Bearing Fault Detection Method Based on Curvelet Transform

Wang

Zhao³

et al. 2021

IEEE Access

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Detection for Incipient Damages of Wind Turbine Rolling Bearing Based on VMD-AMCKD Method

Cited by 33 publications

References 26 publications

Multistage Fault Feature Extraction of Consistent Optimization for Rolling Bearings Based on Correlated Kurtosis

Multistage Fault Feature Extraction of Consistent Optimization for Rolling Bearings Based on Correlated Kurtosis

Feature Extraction Based on EWT With Scale Space Threshold and Improved MCKD for Fault Diagnosis

Novel Adaptive Sparse-Spike Deconvolution Bearing Fault Detection Method Based on Curvelet Transform

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