A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs) by using local characteristic-scale decomposition (LCD) method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD) techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM) classifier based on Chemical Reaction Optimization (CRO) algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.

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“…In the proposed CRO-SVM method, these variables needing optimizing are and , and the fitness function is the test error of the SVM [26]. That is,…”

Section: Cro-svm Methodmentioning

confidence: 99%

Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

Luo

Cheng

2015

Shock and Vibration

Self Cite

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“…Any two decomposed ISCs are mutually independent, with instantaneous frequency of physical significance. ISC needs to meet the following two conditions [12,13].…”

Section: Ensemble Local Meanmentioning

confidence: 99%

“…However, LMD itself also has a large amount of iterative computation and problems associated with end effects [10,11]. Recently, Cheng et al proposed a new selfadaptive signal processing method, local characteristic-scale decomposition (LCD), which can decompose a nonstationary signal into several intrinsic scale components (ISCs) [12][13][14]. By analyzing each ISC, characteristic information of the original signal can be extracted effectively with higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Due to superior time-frequency analysis performance, LCD method has been widely used to analyze nonstationary signals in mechanical fault diagnosis. As with the EMD method, LCD method also causes modemixing effect [13]. Therefore, an improved LCD method, ensemble local characteristic-scale decomposition (ELCD) method, has been proposed to decompose vibration signals.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

Liang

Dong-min

Daidi

et al. 2018

Shock and Vibration

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A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and relative entropy of intrinsic scale components were calculated. According to the distance-based evaluation approach, sensitivity features can be extracted. Finally, sensitivity features were input to extreme learning machine to identify rolling bearing fault types. Experimental results show that the proposed method achieved better performance than support vector machine (SVM) and backpropagation (BP) neural network methods.

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“…Compared with the traditional pattern recognition methods, SVM is more suitable for dealing with nonlinear and high dimensional pattern recognition problems with small samples. For the above reason [7][8][9][10][11], SVM is very suitable for Wa syllables pattern recognition under the small sample condition. However, in the process of recognizing Wa syllables by SVM, penalty parameters and kernel function parameters will directly affect the recognition ability to SVM.…”

Section: Introductionmentioning

confidence: 99%

Wa Language Syllables Classification by Support Vector Machine Based on Genetic Algorithm

Fu¹,

Su²,

Pan³

2017

dtcse

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Abstract. In this paper, propose the method to classify Wa syllables by support vector machine (SVM) based on genetic algorithm (GA). Firstly, select all the Wa syllables from the speech corpus recorded. Secondly, extract pitch and formant frequencies by vowel main body extension method and linear predictive coding method respectively. Thirdly, describe every syllable with a 12-dimensional feature vector composed of its mean values, minima and maxima, belonging to its pitch frequency sequence and its first three formant frequency sequences respectively. Fourthly, divide the obtained data set into training set and test set, the former use to train SVM, and the latter test SVM. Fifthly, normalize data set into [0,1], and select the radial basis function as kernel function. Finally, use the K crossover validation method (K-CV) based on genetic algorithm (GA) to get the best parameter values train SVM. As a result, the predictive accuracy of Wa syllables classification by test set arrives at 84.83%, which has shown that the proposed method is effective and feasible on Wa syllables classification.

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A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

Cited by 40 publications

References 43 publications

Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

Wa Language Syllables Classification by Support Vector Machine Based on Genetic Algorithm

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