The support vector machine parameter optimization method based on artificial chemical reaction optimization algorithm and its application to roller bearing fault diagnosis

Ao, HungLinh; Cheng, Junsheng; Yu, Yang; Truong, Tung Khac

doi:10.1177/1077546313511841

Cited by 33 publications

(28 citation statements)

References 29 publications

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“…To improve the identification ability of the fault classifier, the foremost choice for determining optimal parameters to obtain a higher classification rate is algorithm optimization. Ao [19] proposed an artificial chemical reaction optimization algorithm to optimize SVM parameters for the purpose of bearing fault diagnosis. Cerrada [20] used a genetic algorithm (GA) to enhance the identification accuracy of the random forest classifier.…”

Section: Introductionmentioning

confidence: 99%

Motion Sequence Decomposition-Based Hybrid Entropy Feature and Its Application to Fault Diagnosis of a High-Speed Automatic Mechanism

Wang¹,

Hong-xia²,

Du³

2017

Entropy

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High-speed automatic weapons play an important role in the field of national defense. However, current research on reliability analysis of automaton principally relies on simulations due to the fact that experimental data are difficult to collect in real life. Different from rotating machinery, a high-speed automaton needs to accomplish complex motion consisting of a series of impacts. In addition to strong noise, the impacts generated by different components of the automaton will interfere with each other. There is no effective approach to cope with this in the fault diagnosis of automatic mechanisms. This paper proposes a motion sequence decomposition approach combining modern signal processing techniques to develop an effective approach to fault detection in high-speed automatons. We first investigate the entire working procedure of the automatic mechanism and calculate the corresponding action times of travel involved. The vibration signal collected from the shooting experiment is then divided into a number of impacts corresponding to action orders. Only the segment generated by a faulty component is isolated from the original impacts according to the action time of the component. Wavelet packet decomposition (WPD) is first applied on the resulting signals for investigation of energy distribution, and the components with higher energy are selected for feature extraction. Three information entropy features are utilized to distinguish various states of the automaton using empirical mode decomposition (EMD). A gray-wolf optimization (GWO) algorithm is introduced as an alternative to improve the performance of the support vector machine (SVM) classifier. We carry out shooting experiments to collect vibration data for demonstration of the proposed work. Experimental results show that the proposed work in this paper is effective for fault diagnosis of a high-speed automaton and can be applied in real applications. Moreover, the GWO is able to provide a competitive diagnosis result compared with the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm.

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Section: Introductionmentioning

confidence: 99%

Motion Sequence Decomposition-Based Hybrid Entropy Feature and Its Application to Fault Diagnosis of a High-Speed Automatic Mechanism

Wang¹,

Hong-xia²,

Du³

2017

Entropy

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“…The training of ANNs always needs a large number of samples which may be difficult or even impossible to achieve in practical applications, especially for the fault ones. While SVMs based on statistical learning theory, which is of specialties for a smaller number of samples, have better generalization than ANNs and ensure that the local and global optimal solution are exactly the same [24]. However, the accuracy of a support vector machine (SVM) classifier is highly decided by the selection of optimal parameters for SVMs [24,25].…”

Section: Introductionmentioning

confidence: 99%

A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory

Cao

Ying

et al. 2016

PLoS ONE

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Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing fault pattern recognition. The experimental results show that the proposed method can identify various bearing fault types as well as severities effectively and accurately.

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“…Nowadays, the oil well parameters used in data analysis algorithm are relatively simple, in lack of polyphyletic parameters, evaluation standard and data redundancy [2]. Moreover, with some oil wells entered middle or later periods of high water cut stage, the features like low permeability and resistivity of complicated accumulation layer can cause general manual analysis and linear analysis invalid [3]. In the angle of intelligent machine learning, we propose a nonlinear SVM classification algorithm in this paper, building up the structure of data development system and pattern recognition model of polyphyletic parameters, using SVM through high-dimensional feature space map and hyperplane optimized classification can solve oil well nonlinear parameters analysis and pattern recognition issue.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithm For Efficiency Management Of Oil Well

Bao¹,

Ding²,

Wu³

et al. 2016

Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

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Abstract.Based on machine learning technique and oil well efficiency project practical problem, to the complicated circumstance of oil well efficiency, non-linear machine learning support vector machines ( SVM ) shows a better analysis results than the classified prediction result of linear machine learning logistics regression ( LR ). This paper analyzed and derived the theorems and classification reason of logistics regression and support vector machines. The experiments calculated and compared the accuracies of these two algorithms under the same conditions, the result conforms the conclusion.

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The support vector machine parameter optimization method based on artificial chemical reaction optimization algorithm and its application to roller bearing fault diagnosis

Cited by 33 publications

References 29 publications

Motion Sequence Decomposition-Based Hybrid Entropy Feature and Its Application to Fault Diagnosis of a High-Speed Automatic Mechanism

Motion Sequence Decomposition-Based Hybrid Entropy Feature and Its Application to Fault Diagnosis of a High-Speed Automatic Mechanism

A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory

Machine Learning Algorithm For Efficiency Management Of Oil Well

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