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A platform for research: civil engineering, architecture and urbanism
Roller Bearing Fault Diagnosis Method Based on Chemical Reaction Optimization and Support Vector Machine
Support vector machine (SVM) parameter optimization has always been a demanding task in machine learning. The chemical reaction optimization (CRO) method is an established metaheuristic for the optimization problem and is adapted to optimize the SVM parameters. In this paper, a SVM parameter optimization method based on CRO (CRO-SVM) is proposed. The CRO-SVM classifier is applied to some real-world benchmark data sets, and promising results are obtained. Furthermore, the CRO-SVM is applied to diagnose the roller bearing fault by combining with the local characteristic–scale decomposition (LCD) method. The experimental results show that the combination of CRO-SVM classifiers and the LCD method obtains higher classification accuracy and lower cost time compared to the other methods.
Roller Bearing Fault Diagnosis Method Based on Chemical Reaction Optimization and Support Vector Machine
Support vector machine (SVM) parameter optimization has always been a demanding task in machine learning. The chemical reaction optimization (CRO) method is an established metaheuristic for the optimization problem and is adapted to optimize the SVM parameters. In this paper, a SVM parameter optimization method based on CRO (CRO-SVM) is proposed. The CRO-SVM classifier is applied to some real-world benchmark data sets, and promising results are obtained. Furthermore, the CRO-SVM is applied to diagnose the roller bearing fault by combining with the local characteristic–scale decomposition (LCD) method. The experimental results show that the combination of CRO-SVM classifiers and the LCD method obtains higher classification accuracy and lower cost time compared to the other methods.
Roller Bearing Fault Diagnosis Method Based on Chemical Reaction Optimization and Support Vector Machine
Ao, HungLinh (author) / Cheng, Junsheng (author) / Zheng, Jinde (author) / Truong, Tung Khac (author)
2014-07-08
Article (Journal)
Electronic Resource
Unknown
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