Bearing faults diagnostics based on hybrid LS-SVM and EMD method

Liu, Xiaofeng; Lin, Bo; Luo, Honglin

doi:10.1016/j.measurement.2014.09.037

Cited by 118 publications

(54 citation statements)

References 18 publications

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“…In order to further evaluate the influence of the proposed feature evaluation and weighting scheme to the follow-up classification assignment, an intelligent classifier, namely Least Squares Support Vector Machines (LS-SVM) is employed in the proposed fault diagnosis procedure of rolling element bearing due to its faster solution speed and highersolution accuracy by comparedwith traditional SVM [7].…”

Section: ) Cdet_wfs (C) Mi_wfs (D) Pcc_wfs (E) Pwfsmentioning

confidence: 99%

A New Feature Evaluation and Weighting Method Using Three measurement Hybrid Model and Its Application to Fault Diagnosis of Rolling Element Bearing

Wei¹,

Wang²,

Ren³

2018

AJMSM

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Section: ) Cdet_wfs (C) Mi_wfs (D) Pcc_wfs (E) Pwfsmentioning

confidence: 99%

A New Feature Evaluation and Weighting Method Using Three measurement Hybrid Model and Its Application to Fault Diagnosis of Rolling Element Bearing

Wei¹,

Wang²,

Ren³

2018

AJMSM

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“…Numerous previous studies have reported about signal processing [9][10][11][12][13], like Fourier Transform [9] and wavelet transform [10]. But these methods should select appropriate base functions in advance.…”

Section: Introductionmentioning

confidence: 99%

“…It is difficult to get effective analysis results, because the data from real world machines are nonstationary and nonlinear. Empirical mode decomposition (EMD), as a formidable and effective time-frequency analysis method, is programed to analyze the nonstationary signals and can be adaptive to decompose 2 Shock and Vibration the confusion signal into intrinsic mode functions (IMFs) by the inherent characteristics of the signals [11][12][13]. Features extraction by EMD is appropriate for distinguishing different mechanical signals [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Support vector machine (SVM) has been widely used in recognizing multiple faults for its excellent performance in the practical application. Unlike neural network methods, SVM has great generalization ability of dealing with small samples since its model complexity does not depend on the number of features and thus is suitable for high dimensional data [13,21,22].…”

Section: Introductionmentioning

confidence: 99%

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Multiple‐Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

Zhang

Cai

Cheng

2018

Shock and Vibration

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Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is still a challenge in fault diagnosis. In this paper, a novel method called multiscale feature extraction (MFE) and multiclass support vector machine (MSVM) with particle parameter adaptive (PPA) is proposed. MFE is used to preprocess the process signals, which decomposes the data into intrinsic mode function by empirical mode decomposition method, and instantaneous frequency of decomposed components was obtained by Hilbert transformation. Then, statistical features and principal component analysis are utilized to extract significant information from the features, to get effective data from multiple faults. MSVM method with PPA parameters optimization will classify the fault patterns. The results of a case study of the rolling bearings faults data from Case Western Reserve University show that (1) the proposed intelligent method (MFE PPA MSVM) improves the classification recognition rate; (2) the accuracy will decline when the number of fault patterns increases; (3) prediction accuracy can be the best when the training set size is increased to 70% of the total sample set. It verifies the method is feasible and efficient for fault diagnosis.

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“…A method integrated Least Squares Support Vector Machines and EMD was proposed to diagnose bearing fault by Liu [1]. Dragomiretskiy et al [2] proposed a new multi-component signal quasi-orthogonal decomposition method named variational mode decomposition (VMD).…”

Section: Introductionmentioning

confidence: 99%

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Tang¹,

Liu²

2017

Proceedings of the 2016 4th International Conference on Renewable Energy and Environmental Technology (ICREET 2016)

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Abstract.A novel method based on variational modal decomposition (VMD), improved multiscale permutation entropy (IMPE) and probabilistic neural network (PNN) is proposed to solve the problem of the rolling bearing fault pattern recognition for wind turbine. Firstly, the vibration signal is decomposed into several components using VMD. Then, the IMPE of the optimal component which has the maximum kurtosis is computed and constructed to feature vector. Finally, the feature vector is inputted into PNN classifier to train and test the fault pattern respectively. Experimental analysis results show that the proposed method can effectively identify the damage locations and different damage degree for bearing. It has good engineering application value.

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Bearing faults diagnostics based on hybrid LS-SVM and EMD method

Cited by 118 publications

References 18 publications

A New Feature Evaluation and Weighting Method Using Three measurement Hybrid Model and Its Application to Fault Diagnosis of Rolling Element Bearing

A New Feature Evaluation and Weighting Method Using Three measurement Hybrid Model and Its Application to Fault Diagnosis of Rolling Element Bearing

Multiple‐Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

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