Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines

Zheng, Jianhua; Pan, Haiyang; Cheng, Junsheng

doi:10.1016/j.ymssp.2016.09.010

Cited by 315 publications

(155 citation statements)

References 31 publications

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“…The ensemble classifier not only solves the multi-fault classification problem but also significantly improves the classification performance compared with the single SVM [76,77]. Zheng, et al [78], proposed composite multiscale FuzzyEn and ESVM for rolling bearing fault diagnosis. FSVM was also used to solve multi-classification problems [79].…”

Section: Svmmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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Section: Svmmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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“…Rotating machinery is generally in the state of heavy work. Seriously, equipment performance is affected by bearing failure and even damaged [2]. erefore, it is necessary to study the fault diagnosis of bearing.…”

Section: Introductionmentioning

confidence: 99%

A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm

Fan

Zhang

Xue³

et al. 2020

Shock and Vibration

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In this paper, a novel model for fault detection of rolling bearing is proposed. It is based on a high-performance support vector machine (SVM) that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO). The fundamental of multikernel least square support vector machine (MK-LS-SVM) is overviewed to identify a classifier that allows multidimension features from empirical mode decomposition (EMD) to be fused with high generalization property. Then the multidimension parameters of the MK-LS-SVM are configured by the SRPSO for further performance improvement. Finally, the proposed model is evaluated through experiments and comparative studies. The results prove its effectiveness in detecting and classifying bearing faults.

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“…1,2 In recent years, many fault diagnosis methods have emerged in the field of fault diagnosis, such as the sparse decomposition method and empirical mode decomposition (EMD) method, which are widely used in the analysis of mechanical vibration signals. [3][4][5][6][7][8][9][10][11] Recently, a new method was proposed by YF Peng and JS Chen, which is called the adaptive sparsest narrow-band decomposition (ASNBD) method. [12][13][14] The ASNBD method is based on the idea of the sparse method and adaptive and sparsest time-frequency analysis (ASTFA) method, and combined with a filter.…”

Section: Introductionmentioning

confidence: 99%

Complementary ensemble adaptive sparsest narrow-band decomposition method and its applications to the gear crack fault diagnosis

Cheng

Chen

Luo

et al. 2020

Advances in Mechanical Engineering

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Adaptive sparsest narrow-band decomposition is the most sparse solution to search for signals in the over-complete dictionary library containing intrinsic mode functions, which transform the signal decomposition into an optimization problem, but the calculation accuracy must be improved in the case of strong noise interference. Therefore, in combination with the algorithm of the complementary ensemble empirical mode decomposition, a new method of the complementary ensemble adaptive sparsest narrow-band decomposition is obtained. In the complementary ensemble adaptive sparsest narrow-band decomposition, the white noise opposite to the paired symbol is added to the target signal to reduce the reconstruction error and realize the adaptive decomposition of the signal in the process of optimizing the filter parameters. The analysis results of the simulation and experimental data show this method is superior to complementary ensemble empirical mode decomposition and adaptive sparsest narrow-band decomposition in inhibiting the mode confusion, endpoint effect, improving the component orthogonality and accuracy, and effectively identifying the gears fault types.

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Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines

Cited by 315 publications

References 31 publications

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm

Complementary ensemble adaptive sparsest narrow-band decomposition method and its applications to the gear crack fault diagnosis

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