Bearing Fault Diagnosis in the Mixed Domain Based on Crossover-Mutation Chaotic Particle Swarm

The classification frameworks for fault diagnosis of rolling element bearings in rotating machinery are mostly based on analysis in a single time-frequency domain, where sensitive features are not completely extracted. To solve this problem, a new fault diagnosis technique is proposed in the mixed domain, based on the crossover-mutation chaotic particle swarm optimization support vector machine. Firstly, fault features are generated using techniques in the time domain, the frequency domain, and the time-frequency domain. Secondly, the weighted maximum relevance minimum redundancy (WMRMR) algorithm is adopted to reduce the dimension of the feature set and to establish the representative feature set. Thirdly, a new crossover-mutation strategy is suggested to reduce the local minima in optimization, and an optimization disturbance is added. Finally, the support vector machine is optimized using the improved chaotic particle swarm to improve fault classification diagnosis. The effectiveness of the proposed new bearing fault diagnostic technique is verified by experimental tests under different bearing conditions. Test results showed that the bearing fault classification accuracy of CMCPSO-SVM in the mixed domain was much higher than those in a single feature domain.

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Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier

Sensors ◽

10.3390/s20123422 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3422 ◽

Cited By ~ 4

Author(s):

Mien Van ◽

Duy Tang Hoang ◽

Hee Jun Kang

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Least Squares ◽

Particle Swarm ◽

Fault Classification ◽

Support Vector ◽

Swarm Optimization ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Bearing is one of the key components of a rotating machine. Hence, monitoring health condition of the bearing is of paramount importace. This paper develops a novel particle swarm optimization (PSO)-least squares wavelet support vector machine (PSO-LSWSVM) classifier, which is designed based on a combination between a PSO, a least squares procedure, and a new wavelet kernel function-based support vector machine (SVM), for bearing fault diagnosis. In this work, bearing fault classification is transformed into a pattern recognition problem, which consists of three stages of data processing. Firstly, a rich information dataset is built by extracting the features from the signals, which are decomposed by the nonlocal means (NLM) and empirical mode decomposition (EMD). Secondly, a minimum-redundancy maximum-relevance (mRMR) method is employed to determine a subset of feature that can provide an optimal performance. Thirdly, a novel classifier, namely LSWSVM, is proposed with the aid of a PSO, to provide higher classification accuracy. The key innovative science of this work is to propropose a new classifier with the aid of an new wavelet kernel type to increase the classification precision of bearing fault diagnosis. The merit features of the proposed approach are demonstrated based on a benchmark bearing dataset and a comprehensive comparison procedure.

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A Bearing Fault Diagnosis Method Based on Autoencoder and Particle Swarm Optimization – Support Vector Machine

Intelligent Computing Theories and Application - Lecture Notes in Computer Science ◽

10.1007/978-3-319-95930-6_28 ◽

2018 ◽

pp. 298-308 ◽

Cited By ~ 3

Author(s):

Duy-Tang Hoang ◽

Hee-Jun Kang

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Particle Swarm ◽

Support Vector ◽

Swarm Optimization ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method

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Application of Xgboost Feature Extraction in Fault Diagnosis of Rolling Bearing

Mechanical Engineering Science ◽

10.33142/me.v1i2.1659 ◽

2020 ◽

Vol 1 (2) ◽

Author(s):

Xingang WANG ◽

Chao WANG

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Fault Diagnosis ◽

Frequency Domain ◽

Time Domain ◽

Vibration Signal ◽

Rolling Bearing ◽

Support Vector ◽

Time Frequency ◽

Diagnosis Accuracy

Due to the difficulty that excessive feature dimension in fault diagnosis of rolling bearing will lead to the decrease of classification accuracy, a fault diagnosis method based on Xgboost algorithm feature extraction is proposed. When the Xgboost algorithm classifies features, it generates an order of importance of the input features. The time domain features were extracted from the vibration signal of the rolling bearing, the time-frequency features were formed by the singular value of the modal components that were decomposed by the variational mode decomposition. Firstly, the extracted time domain and time-frequency domain features were input into the support vector machine respectively to observe the fault diagnosis accuracy. Then, Xgboost algorithm was used to rank the importance of features and got the accuracy of fault diagnosis. Finally, important features were extracted and the extracted features were input into the support vector machine to observe the fault diagnosis accuracy. The result shows that the fault diagnosis accuracy of rolling bearing is improved after important feature extraction in time domain and time-frequency domain by Xgboost.

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Bearing Fault Diagnosis Based on Labview

International Journal of Advanced Pervasive and Ubiquitous Computing ◽

10.4018/ijapuc.2015070103 ◽

2015 ◽

Vol 7 (3) ◽

pp. 25-37

Author(s):

Wan-ye Yao ◽

Xue-Li Jiang

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Energy Spectrum ◽

Wavelet Packet ◽

Particle Swarm ◽

Support Vector ◽

Particle Swarm Algorithm ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Factor C

This function of wavelet packet decomposition and the energy of each band to strike is achieved within the Labview module. Signal energy in different frequency bands within the change reflects a change in the operating state. Extract wavelet packet energy spectrum of each band, making it as a feature vector. Finally the fault are classified by SVM. The two Parameters, the kernel function parameters g of radial machine support vector machine and penalty factor C of the radial machine support vector machine, are optimally chosen, automatically and rapidly, by using the method of particle swarm algorithm, avoiding the blindness of artificial selection parameters. The Matalab program of support vector machines based on particle swarm optimization are made into COM components. Mixed programming, Labview call COM component, generated by the M file, is implemented, which is divorced from the MATLAB environment, making it good for expanding the function of Labview. The effectiveness, wavelet packet energy spectrum - PSOSVM model of the bearing fault diagnosis, is verified.

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Bearing fault diagnosis based on improved particle swarm optimized VMD and SVM models

Advances in Mechanical Engineering ◽

10.1177/16878140211028451 ◽

2021 ◽

Vol 13 (6) ◽

pp. 168781402110284

Author(s):

Qingfeng Zhang ◽

Shuang Chen ◽

Zhan Peng Fan

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Particle Swarm ◽

Vibration Signal ◽

Support Vector ◽

Data Set ◽

Swarm Optimization ◽

Bearing Fault ◽

Bearing Fault Diagnosis

To improve the accuracy of fault diagnosis of bearing, the improved particle swarm optimization variational mode decomposition (VMD) and support vector machine (SVM) models are proposed. Aiming at the convergence effect of particle swarm optimization (PSO), dynamic inertia weight, and gradient information are introduced to improve PSO (IPSO). IPSO is used to optimize the optimal number of VMD modal components and the penalty factor, which is applied to the vibration signal decomposition. The fault sample set is constructed by calculating the multi-scale information entropy of each component signal obtained from the bearing vibration signals. At the same time, IPSO is used to optimize the support vector machine (IPSO-SVM), which is used to bearing fault diagnosis. The time-domain feature data set is used as the comparison data set, and the classical PSO, genetic algorithm, and cross-validation method are used as the comparison algorithm to verify the effectiveness of the method in this paper. The research results show that the optimized VMD can effectively decompose the vibration signal and can effectively highlight the fault characteristics. IPSO can increase the accuracy by 2% without adding additional costs. And the accuracy, volatility, and convergence error of IPSO are better than comparison algorithms.

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Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals

ISA Transactions ◽

10.1016/j.isatra.2020.12.054 ◽

2021 ◽

Author(s):

Zhenya Wang ◽

Ligang Yao ◽

Gang Chen ◽

Jiaxin Ding

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Rolling Bearing ◽

Permutation Entropy ◽

Support Vector ◽

Complex Signals ◽

Machine Method ◽

Support Vector Machine Method ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽

10.3390/electronics10121496 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1496

Author(s):

Hao Liang ◽

Yiman Zhu ◽

Dongyang Zhang ◽

Le Chang ◽

Yuming Lu ◽

...

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Mutual Information ◽

Analog Circuit ◽

Fault Classification ◽

Support Vector ◽

Svm Classifier ◽

Fault Parameters ◽

Diagnosis Method ◽

Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

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

Shock and Vibration ◽

10.1155/2014/825825 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 25

Author(s):

HungLinh Ao ◽

Junsheng Cheng ◽

Kenli Li ◽

Tung Khac Truong

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Roller Bearing ◽

Local Characteristic ◽

Support Vector ◽

Svm Classifier ◽

Outer Race ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Energy Entropy

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

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