Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering‐Enhanced Multiscale Entropy Features

Zhang, Jinbao; Zhao, Yongqiang; Li, Xinglin; Liu, Ming

doi:10.1155/2019/7905674

Cited by 10 publications

(5 citation statements)

References 34 publications

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“…Compared with Zheng et al and Gao et al, 33,34 the proposed method can distinguish more rolling bearing fault states when the classification accuracy is the same as that of test group A; Compared with Tang et al, 35 there is no need for preprocessing and feature selection of rolling bearing fault signals. Although the classification accuracy of test group B is slightly lower than that of literature, 36,37 the number of classification of fault states is significantly increased.…”

Section: Experimental Validationmentioning

confidence: 67%

Rolling bearing state assessment based on the composite multiscale weight slope entropy and hierarchical prototype-based approach

Zhang¹,

Pan

Zhang³

et al. 2022

Advances in Mechanical Engineering

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To evaluate the state of rolling bearing more accurately, a new feature called composite multiscale weight slope entropy was proposed for the complexity measurement of vibration signals. On the basis of analyzing the fault signal structure, the new feature could consider the influence of the nonlinearity, the multiscale characteristics, the fluctuation of the amplitude, and the amplitude itself on the fault signals. In the following, different fault types and the corresponding damage degree of rolling bearings were identified with the hierarchical prototype-based approach. Compared with the results of different modified slope entropy, it is shown that composite multiscale weight slope entropy could significantly improve the identification accuracy. In the two designed testing schemes, ten and sixty state types of rolling bearings are respectively calculated, and the identification accuracy could reach up to 100% and 96.5% respectively, which illustrate the effectiveness and the validity of the proposed approach.

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Section: Experimental Validationmentioning

confidence: 67%

Rolling bearing state assessment based on the composite multiscale weight slope entropy and hierarchical prototype-based approach

Zhang¹,

Pan

Zhang³

et al. 2022

Advances in Mechanical Engineering

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“…The research work of Gu et al [ 54 ] uses the minimum average envelope entropy with the Teager energy operator method to diagnose incipient faults on bearings. Zhang et al [ 55 ] decomposed vibration signals from a bearing at speeds ranging from 1720 to 1797 rpm into intrinsic mode functions, to later calculate several multiscale entropy indicators, and to finally diagnose the bearings. Qin et al [ 56 ] used EMD and the energy entropy to select the intrinsic mode functions (IMF) component for feature extraction and subsequently bearing diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Sandoval

Leturiondo

Vidal

et al. 2021

Sensors

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To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis.

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“…Adaptive local iterative filtering (ALIF) constructs an adaptive filter using the Fokker-Planck equation; it can effectively solve the noise sensitivity and modal aliasing problems of the adaptive-decomposition algorithm. In [14], ALIF was used to decompose the vibration signal, and the best IMF was selected according to the multiscale entropic features to realize bearing-fault diagnosis. In [15], the ALIF was combined with a Teager-Kaiser energy operator (TKEO) to realize the characteristic fault-frequency extraction of early faults in rolling bearing elements.…”

Section: Introductionmentioning

confidence: 99%

Weighted kurtosis-based VMD and improved frequency-weighted energy operator low-speed bearing-fault diagnosis

Xiao

Wang²,

Peng

2020

Meas. Sci. Technol.

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The diagnosis of low-speed bearing faults remains a challenging issue because background noise is often present and the impulse signal is prone to being masked. In this paper, we propose a low-speed bearing-fault diagnosis method using weighted-kurtosis variational-mode decomposition and an improved frequency-weighted energy operator (IFWEO). First, the raw signal is decomposed using VMD, and WK is employed to select the optimum intrinsic mode function to reconstruct the signal. The reconstructed signal carries abundant fault information. Second, a third-order cumulant method is introduced to improve the FWEO, and this method is able to strengthen the signal impulse and enhance the fault features. The IFWEO is able to effectively reduce the effects of noise. Third, the effectiveness of the proposed method is validated by simulation and engineering experiments, and the results show that the method presented here is able to accurately diagnose low-speed bearing faults.

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Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering‐Enhanced Multiscale Entropy Features

Cited by 10 publications

References 34 publications

Rolling bearing state assessment based on the composite multiscale weight slope entropy and hierarchical prototype-based approach

Rolling bearing state assessment based on the composite multiscale weight slope entropy and hierarchical prototype-based approach

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Weighted kurtosis-based VMD and improved frequency-weighted energy operator low-speed bearing-fault diagnosis

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