An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing

An, Guoqing; Tong, Qingbin; Zhang, Yanan; Liu, Ruifang; Li, Weili; Cao, Junci; Lin, Yi

doi:10.3390/en14041079

Cited by 13 publications

(6 citation statements)

References 41 publications

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“…As this paper addresses the fault feature extraction of rolling bearings with noise pollution, the selection of the components to be used in the next step of the analysis needs to ensure that the selected SSC components contain sufficient fault shock information. The kurtosis index is the response to the shock features of the vibration signal [38]. A higher kurtosis value indicates that the shock characteristics of the signal are more prominent, which laterally reflects a lower level of interference from noise.…”

Section: Improved Ssd Algorithm Based On Permutation Entropymentioning

confidence: 99%

Rolling bearing fault feature extraction via improved SSD and a singular-value energy autocorrelation coefficient spectrum

Xu¹,

Shen²,

Jiang³

et al. 2022

Meas. Sci. Technol.

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It is usually difficult in extracting weak fault features from rolling bearing vibration signals under noise pollution. Aiming to this problem, a fault feature extraction approach for rolling bearing using improved singular spectrum decomposition (SSD) and singular value energy autocorrelation coefficient spectrum (SVEACS) is proposed. Firstly, to facilitate the determination of the optimal modal parameters in the singular spectrum decomposition algorithm, the number of singular spectrum decomposition layers is adaptively determined with an improved SSD algorithm based on permutation entropy. Then the optimal modal components are selected, and the proposed SVEACS is used to determine the order of singular value noise reduction. Finally, the envelope analysis is used to extract the accurate shock characteristics of the denoised signal. The results of the experiments on simulation and real signals indicate that, the proposed method can effectively extract the weak characteristics of the vibration signal under strong noise, and accurately diagnose the fault of rolling bearing.

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Section: Improved Ssd Algorithm Based On Permutation Entropymentioning

confidence: 99%

Rolling bearing fault feature extraction via improved SSD and a singular-value energy autocorrelation coefficient spectrum

Xu¹,

Shen²,

Jiang³

et al. 2022

Meas. Sci. Technol.

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“…The vibration signals of the wind turbine HSSB are decomposed by CEEMD, and the sum of a group of IMF components and residual terms are obtained [35][36][37]. The first n IMF energies are calculated as follows:…”

Section: Time-frequency Domain Featuresmentioning

confidence: 99%

Health Assessment and Remaining Useful Life Prediction of Wind Turbine High-Speed Shaft Bearings

Zhang

Kari

et al. 2021

Energies

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Vibration signals contain abundant information that reflects the health status of wind turbine high-speed shaft bearings ((HSSBs). Accurate health assessment and remaining useful life (RUL) prediction are the keys to the scientific maintenance of wind turbines. In this paper, a method based on the combination of a comprehensive evaluation function and a self-organizing feature map (SOM) network is proposed to construct a health indicator (HI) curve to characterizes the health state of HSSBs. Considering the difficulty in obtaining life cycle data of similar equipment in a short time, the exponential degradation model is selected as the degradation trajectory of HSSBs on the basis of the constructed HI curve, the Bayesian update model, and the expectation–maximization (EM) algorithm are used to predict the RUL of HSSBs. First, the time domain, frequency domain, and time–frequency domain degradation features of HSSBs are extracted. Second, a comprehensive evaluation function is constructed and used to select the degradation features with good performance. Third, the SOM network is used to fuse the selected degradation features to construct a one-dimensional HI curve. Finally, the exponential degradation model is selected as the degradation trajectory of HSSBs, and the Bayesian update and EM algorithm are used to predict the RUL of the HSSB. The monitoring data of a wind turbine HSSB in actual operation is used to validate the model. The HI curve constructed by the method in this paper can better reflect the degradation process of HSSBs. In terms of life prediction, the method in this paper has better prediction accuracy than the SVR model.

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“…Chen et al 26 proposed the WOA-VMD and improved threshold noise reduction method for the early fault characteristic extraction of rolling bearings, and established the optimal mode component selection criteria of L-kurtosis and correlation coefficient. An et al 27 established an improved VMD method based on SFLA algorithm, and constructed a multi-objective evaluation function to select the optimal mode component based on envelope spectrum entropy, kurtosis and correlation coefficient. In WOA-VMD and the improved VMD methods, although the objective functions consider the correlation between the decomposed modes and original signal, the kurtosis index used may be affected by a few random pulses, which makes the impact signal deviate from the fault frequency, hence there will be misjudgment in the choice of the best frequency-band.…”

Section: Introductionmentioning

confidence: 99%

Novel variational mode decomposition method for rotating machinery fault diagnosis based on weighted correlated kurtosis and salp swarm algorithm

2023

Noise & Vibration Worldwide

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The mechanical vibration response in engineering is the superimposition of multi-frequency characteristic information. Therefore, it is of great necessity to utilize signal decomposition methods to extract fault characteristics for ultimate diagnosis. In the traditional variational mode decomposition (VMD) methods, the decomposition parameters (i.e. the mode number and quadratic penalty factor) are determined according to the principle of convenience and experience. This behavior reduces the performance of VMD methods to a great extent, and limits their decomposition accuracy and feature extraction capability. To resolve this problem, a novel VMD method for rotating machinery fault diagnosis is developed in this article. Firstly, a measurement index called weighted correlated kurtosis ( WCK) is constructed by combining correlated kurtosis and Pearson correlation coefficient. Secondly, taking the maximum WCK as the goal function, salp swarm algorithm is utilized to find the optimum parameters. Lastly, the feature extraction is performed according to the selected sensitive mode possessing the maximum WCK. Two experimental examples demonstrate the effectiveness of the developed VMD method on mechanical vibration signal processing and fault diagnosis. Furthermore, by comparing with other two typical VMD methods, the superiority of the developed method is verified.

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An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing

Cited by 13 publications

References 41 publications

Rolling bearing fault feature extraction via improved SSD and a singular-value energy autocorrelation coefficient spectrum

Rolling bearing fault feature extraction via improved SSD and a singular-value energy autocorrelation coefficient spectrum

Health Assessment and Remaining Useful Life Prediction of Wind Turbine High-Speed Shaft Bearings

Novel variational mode decomposition method for rotating machinery fault diagnosis based on weighted correlated kurtosis and salp swarm algorithm

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