Detection for weak fault in planetary gear trains based on an improved maximum correlation kurtosis deconvolution

Zhang, Jianqun; Zhong, Min; Zhang, Jun

doi:10.1088/1361-6501/ab43ed

Cited by 27 publications

(15 citation statements)

References 29 publications

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“…6 The existing fault diagnosis methods can be roughly divided into two categories, depending on the technical routes adopted. One is the traditional fault diagnosis based on spectrum analysis, 7 , 8 and the other is based on the data-driven intelligent fault diagnosis. 9–16…”

Section: Introductionmentioning

confidence: 99%

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An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions

Zhang

Qin

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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To identify rolling bearing faults under variable load conditions, a method named DISA-KNN is proposed in this paper, which is based on the strategy of feature extraction-domain adaptation-classification. To be specific, the time-domain and frequency-domain indicators are used for feature extraction. Discriminative and domain invariant subspace alignment (DISA) is used to minimize the data distributions’ discrepancies between the training data (source domain) and testing data (target domain). K-nearest neighbor (KNN) is applied to identify rolling bearing faults. DISA-KNN’s validation is proved by the experimental signal collected under different load conditions. The identification accuracies obtained by the DISA-KNN method are more than 90% on four datasets, including one dataset with 99.5% accuracy. The strength of the proposed method is further highlighted by comparisons with the other 8 methods. These results reveal that the proposed method is promising for the rolling bearing fault diagnosis in real rotating machinery.

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Section: Introductionmentioning

confidence: 99%

“…For instance, Zhang et al. 8 first applied the signal processing methods to obtain the component signal which contains fault features. Thereafter, the gear fault characteristic frequency was found in the envelope spectrum.…”

Section: Introductionmentioning

confidence: 99%

An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions

Zhang

Qin

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Shen et al [47] proposed a novel signal processing method based on the particle swarm optimization, maximum correlated kurtosis deconvolution, variational, mode decomposition and fast spectral kurtosis (PSO-MCKD-VMD-FSK) to extract fault characteristics for the signal-to-noise ratio and uneven energy distribution problems. Zhang et al [48] proposed an improved maximum correlation kurtosis deconvolution based on grasshopper optimization algorithm. Liang et al [49] employed an optimized Morlet wavelet as the initial filter in the deconvolution process, which contributes to improving both the efficiency and performance of MAKD.…”

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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“…However, rotating machinery is prone to failure, which could damage property and human life. 4,5 Early detection or diagnosis of the potential weak fault in these systems is desirable to cut down the economic loss of industrial production and increase the benefits. 6,7 The vibration signals captured from rotating machinery are mostly non-stationary and consist of multiple-component aliased vibration signals.…”

Section: Introductionmentioning

confidence: 99%

“…To detect these weak faults in planetary gearboxes, a comprehensive diagnostic method called improved maximum correlation kurtosis deconvolution (IMCKD) was proposed by Zhang. 5 The weak root crack in the single-stage planetary sun gear was used to prove its effectiveness. Cheng et al proposed an improved symplectic ge-ometric mode decomposition (ISGMD) that contains the vast majority of fault feature information for the early processing of gear fault signals.…”

Section: Introductionmentioning

confidence: 99%

A New Intelligent Weak Fault Recognition Framework for Rotating Machinery

Zhao¹,

Jia²,

Ding³

et al. 2020

The International Journal of Acoustics and Vibration

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The presence of strong background noises makes it a challenging task to detect weak fault characteristics in vibration signals collected from rotating machinery. Thus, a two-stage intelligent weak fault recognition framework, which includes signal enhancement and intelligent recognition, is proposed in this work. The signal enhancement is accomplished via an optimized relevant variational mode decomposition (ORVMD) algorithm. Specifically, the optimal parameters are derived by combining a particle swarm optimization (PSO) algorithm and the novel defined relevant energy (Re) index. This optimized VMD algorithm can extract the principal components from the raw signals. Then, the enhanced vibration signals via the proposed ORVMD are converted into spectral signals and fed into an improved stacked auto-encoder (ISAE) algorithm for fault recognition. Experimental results demonstrate the effectiveness of the proposed algorithms and fault diagnosis framework in rotating machinery fault recognition and detection.

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Detection for weak fault in planetary gear trains based on an improved maximum correlation kurtosis deconvolution

Cited by 27 publications

References 29 publications

An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions

An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions

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

A New Intelligent Weak Fault Recognition Framework for Rotating Machinery

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