Early Fault Diagnosis of Shaft Crack Based on Double Optimization Maximum Correlated Kurtosis Deconvolution and Variational Mode Decomposition

Ma, Tongwei; Zhang, Xiangfeng; Hong, Jiman; Wang, Kedian; Xia, Lei; Guan, Xiangheng

doi:10.1109/access.2021.3052852

Cited by 11 publications

(5 citation statements)

References 9 publications

(15 reference statements)

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“…VMD iterates to update u k (t) in the frequency domain and extracts the spectrum gravity center as its center frequency ω k . According to References [34][35][36], VMD essentially solves a constrained variational model expressed with…”

Section: Vibration Signal Preprocessing and Fault Feature Extractionmentioning

confidence: 99%

“…, ω K } is the set of frequency centers of IMFs, ∂ t represents a partial derivative of time t, δ(t) is Dirac distribution, the symbol * stands for convolution operator, and • 2 represents Euclidian norm. To solve the above constrained variational model, a quadratic penalty factor α and Lagrange multiplier λ are introduced to transform the constrained optimization problem expressed with Equation ( 1) to an unconstrained one as follows [34][35][36]:…”

Section: Vibration Signal Preprocessing and Fault Feature Extractionmentioning

confidence: 99%

“…With Equation ( 2), the alternating direction method of multipliers is applied to update u k , ω k , and λ iteratively. The updated equations are as follows [35].…”

Section: Vibration Signal Preprocessing and Fault Feature Extractionmentioning

confidence: 99%

“…where the symbol ∧ above the variables represents the updated values of u k , ω k , λ, and v. Additionally, τ is the updating parameter of Lagrange multiplier. More details concerning the implementation process of VMD can be found in References [34][35][36].…”

Section: Vibration Signal Preprocessing and Fault Feature Extractionmentioning

confidence: 99%

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A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

et al. 2021

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In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method.

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Section: Vibration Signal Preprocessing and Fault Feature Extractionmentioning

confidence: 99%

Section: Vibration Signal Preprocessing and Fault Feature Extractionmentioning

confidence: 99%

“…With Equation ( 2), the alternating direction method of multipliers is applied to update u k , ω k , and λ iteratively. The updated equations are as follows [35].…”

Section: Vibration Signal Preprocessing and Fault Feature Extractionmentioning

confidence: 99%

Section: Vibration Signal Preprocessing and Fault Feature Extractionmentioning

confidence: 99%

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A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

et al. 2021

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“…In order to solve the shortcoming of standard minimum entropy de-convolution (MED) which only highlights a few local fault impulse components, a novel maximum correlated kurtosis de-convolution (MCKD) method taking advantage of the periodic nature of the bearing faulty signal was introduced to realize the de-convolution of the periodic impulse of bearing and gear fault signals, and its advantage over standard MED was verified (McDonald et al, 2012). Subsequently, kinds of MCKD based methods have been proposed and used in fault diagnosis widely (Hong et al, 2019; Ma et al, 2021; Wang et al, 2019). To overcome the shortcoming of MED preferring to de-convolving a single-impulse, a non-iterative de-convolution approach named multi-point optimal minimum entropy de-convolution adjusted (MOMEDA) was proposed (McDonald and Zhao, 2017), and this algorithm introduced the time target vector to determine the position and weight of the pulse sequence to be de-convolved, and used the multi-point kurtosis value to determine the fault occurrence period, so as to achieve the extraction of continuous multi-point fault shock pulses.…”

Section: Introductionmentioning

confidence: 99%

Weak feature extraction of rolling element bearing based on self-adaptive blind de-convolution and enhanced envelope spectral

Wang

et al. 2021

Journal of Vibration and Control

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As the most commonly used support component in engineering, rolling element bearing is also the most prone-to-failure part. The vibration signal of faulty bearing will take on repetitive impact and modulation characteristics, and the two features are often difficult to be extracted by conventional fault feature extraction methods such as envelope spectral. The main reasons are due to the influence of strong background noise, the signal attenuation of the acquisition path, and the early weak failure characteristics. To solve the above problem, a weak fault feature extraction method of rolling element bearing by combing improved minimum entropy de-convolution with enhanced envelope spectral is proposed in the paper. The enhancement effect of improved minimum entropy de-convolution on impact features and the satisfactory extraction effect of EES on repetitive impact and modulation features are utilized comprehensively by the proposed method. Firstly, improved minimum entropy de-convolution is used to filter the vibration signal of faulty bearing to enhance the impact characteristics, and the influence of signal acquisition path on the attenuation of the signal characteristics is also weakened at the same time. Then, enhanced envelope spectral is performed on the filtered signal, and the repetitive impact and modulation characteristics of vibration signal are extracted synchronously. In order to solve the shortcomings of the current commonly used de-convolution methods, an improved minimum entropy de-convolution method based on D-norm is proposed, which can solve the interference caused by random impulsive signals effectively. In addition, compared with the conventional method such as envelope spectral, the enhanced envelope spectral method could extract the repetitive impact and modulation characteristics of the faulty signal simultaneously much more effectively. Effectiveness and superiority of the proposed method are verified through simulation, experiment, and engineering application.

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Maximum average impulse energy ratio deconvolution and its application for periodic fault impulses enhancement of rolling bearing

Wang

Zhang

et al. 2022

Advanced Engineering Informatics

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Early Fault Diagnosis of Shaft Crack Based on Double Optimization Maximum Correlated Kurtosis Deconvolution and Variational Mode Decomposition

Cited by 11 publications

References 9 publications

A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

Weak feature extraction of rolling element bearing based on self-adaptive blind de-convolution and enhanced envelope spectral

Maximum average impulse energy ratio deconvolution and its application for periodic fault impulses enhancement of rolling bearing

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