RETRACTED: Rolling bearing fault detection approach based on improved dispersion entropy and AFSA optimized SVM

Liu, Wuqiang; Shen, Jinxing; Yang, Xiaoqiang

doi:10.1177/0020720920940584

Cited by 8 publications

(9 citation statements)

References 10 publications

(9 reference statements)

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“…In order to maximize the sample interval, the optimization formula was constructed and expressed as follows [38]:…”

Section: Svm Algorithmmentioning

confidence: 99%

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A Comprehensive Diagnosis Method of Rolling Bearing Fault Based on CEEMDAN-DFA-Improved Wavelet Threshold Function and QPSO-MPE-SVM

Wang

et al. 2021

Entropy

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A comprehensive fault diagnosis method of rolling bearing about noise interference, fault feature extraction, and identification was proposed. Based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), detrended fluctuation analysis (DFA), and improved wavelet thresholding, a denoising method of CEEMDAN-DFA-improved wavelet threshold function was presented to reduce the distortion of the noised signal. Based on quantum-behaved particle swarm optimization (QPSO), multiscale permutation entropy (MPE), and support vector machine (SVM), the QPSO-MPE-SVM method was presented to construct the fault-features sets and realize fault identification. Simulation and experimental platform verification showed that the proposed comprehensive diagnosis method not only can better remove the noise interference and maintain the original characteristics of the signal by CEEMDAN-DFA-improved wavelet threshold function, but also overcome overlapping MPE values by the QPSO-optimizing MPE parameters to separate the features of different fault types. The experimental results showed that the fault identification accuracy of the fault diagnosis can reach 95%, which is a great improvement compared with the existing methods.

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“…In order to maximize the sample interval, the optimization formula was constructed and expressed as follows [38]:…”

Section: Svm Algorithmmentioning

confidence: 99%

“…In order to maximize the sample interval, the optimization formula was constructed and expressed as follows [ 38 ]:

where w is the weighting of the optimal classification surface. C denotes the penalty parameter of the deviation item

.…”

Section: Fault Feature Extraction and Identification Algorithm Of The Qpso-mpe-svmmentioning

confidence: 99%

A Comprehensive Diagnosis Method of Rolling Bearing Fault Based on CEEMDAN-DFA-Improved Wavelet Threshold Function and QPSO-MPE-SVM

Wang

et al. 2021

Entropy

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“…To calculate the normalized DispEn (NDispEn) according to Equation (7), DispEn is divided by the largest possible DispEn.…”

Section: Dispersion Entropymentioning

confidence: 99%

“…Rostaghi et al investigated the potential applications of DispEn in rotating machinery diagnosis and demonstrated its superiority over PerEn and approximate entropy (ApEn) [3]. Liu et al combined DispEn and wavelet packets to extract the features used for bearing diagnosis [7]. They calculated the DispEn of each wavelet packet.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS

Rostaghi

Khatibi

Ashory

et al. 2021

Entropy

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Bearing vibration signals typically have nonlinear components due to their interaction and coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal complexity at various scales. Hence, measuring signal complexity at different scales is helpful to diagnosis of bearing faults. Numerous studies have investigated multiscale algorithms; nevertheless, multiscale algorithms using the first moment lose important complexity data. Accordingly, generalized multiscale algorithms have been recently introduced. The present research examined the use of refined composite generalized multiscale dispersion entropy (RCGMDispEn) based on the second moment (variance) and third moment (skewness) along with refined composite multiscale dispersion entropy (RCMDispEn) in bearing fault diagnosis. Moreover, multiclass FCM-ANFIS, which is a combination of adaptive network-based fuzzy inference systems (ANFIS), was developed to improve the efficiency of rotating machinery fault classification. According to the results, it is recommended that generalized multiscale algorithms based on variance and skewness be examined for diagnosis, along with multiscale algorithms, and be used to achieve an improvement in the results. The simultaneous usage of the multiscale algorithm and generalized multiscale algorithms improved the results in all three real datasets used in this study.

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“…For example, Fatima et al for the multi-channel vibration signal features of bearing signal, the compensation technology is used to select the sensitive features, and finally a variety of SVM support vector machines are used to classify the signal features [8]. A large number of feature types and the selection of sensitive features make the use of fault classification method based on time-frequency features limited and inefficient [9]. Among a large number of feature extraction methods, a feature extraction method based on speech vibration signal, Mel frequency cepstrum coefficient (MFCC), is noticed [10].…”

Section: Introductionmentioning

confidence: 99%

Feature Classification Method of Frequency Cepstrum Coefficient Based On Weighted Extreme Gradient Boosting

Wang

Zhang

et al. 2021

IEEE Access

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Cepstrum coefficient and decision tree classification provide a feasible method for mechanical fault diagnosis. However, this method has the problems of low accuracy of feature extraction and low reliability of feature classification. The efficiency and classification results of this method are not ideal. Therefore, this paper proposes a feature classification method of frequency cepstrum coefficients based on weighted extreme gradient boosting. The proposed method is mainly divided into two parts: feature extraction and feature classification. In the part of feature extraction, Considering the limitation of Mel frequency scale in MFCC, an adaptive frequency cepstrum coefficient (AFCC) method is proposed, which can adaptively obtain the frequency scale of the corresponding signal, thus improving the feature accuracy extracted by MFCC method. In the aspect of feature classification, XGboost algorithm has strong learning ability, which easily leads to over fitting and affects the classification accuracy, A weighted extreme gradient boosting (WXGB) method is proposed, and an improved distance evaluation method (IDE) is introduced to modify the feature weights learned by XGboost, so as to improve the prediction accuracy of the classification model. In order to verify the effectiveness of the method, the vibration signals of rolling bearings with different fault types are taken as samples to carry out feature extraction and classification. The results of feature extraction and classification of different methods are compared, and the effectiveness of the proposed feature classification method based on weighted extremum gradient boosting frequency cepstrum coefficient in rolling bearing fault diagnosis is verified.

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RETRACTED: Rolling bearing fault detection approach based on improved dispersion entropy and AFSA optimized SVM

Cited by 8 publications

References 10 publications

A Comprehensive Diagnosis Method of Rolling Bearing Fault Based on CEEMDAN-DFA-Improved Wavelet Threshold Function and QPSO-MPE-SVM

A Comprehensive Diagnosis Method of Rolling Bearing Fault Based on CEEMDAN-DFA-Improved Wavelet Threshold Function and QPSO-MPE-SVM

Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS

Feature Classification Method of Frequency Cepstrum Coefficient Based On Weighted Extreme Gradient Boosting

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