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首页> 外文期刊>Journal of vibration and control: JVC >The support vector machine parameter optimization method based on artificial chemical reaction optimization algorithm and its application to roller bearing fault diagnosis
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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

机译：基于人工化学反应优化算法的支持向量机参数优化方法及其在滚动轴承故障诊断中的应用

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摘要

The accuracy of a support vector machine (SVM) classifier is decided by the selection of optimal parameters for SVM. An artificial chemical reaction optimization algorithm (ACROA) is a new method to solve the global optimization problem and is adapted to optimize SVM parameters. In this paper, a SVM parameter optimization method based on ACROA (ACROA-SVM) is proposed. Furthermore, the ACROA-SVM is applied to diagnose roller bearing faults. Firstly, the original modulation roller bearing vibration signals are decomposed into product functions (PFs) by using the local mean decomposition (LMD) method. Secondly, the ratios of amplitudes at the different fault characteristic frequencies in the envelope spectra of some PFs that include dominant fault information are defined as the characteristic amplitude ratios. Finally, the characteristic amplitude ratios are used as input to the ACROA-SVM classifiers, and the fault patterns of the roller bearing are identified. The result shows that the combination of this ACROA-SVM classifiers and LMD method can effectively improve the accurate rate of fault diagnosis and reduce cost time.
机译：支持向量机（SVM）分类器的准确性取决于为SVM选择的最佳参数。人工化学反应优化算法（ACROA）是解决全局优化问题的一种新方法，适用于优化SVM参数。提出了一种基于ACROA的支持向量机参数优化方法（ACROA-SVM）。此外，ACROA-SVM还用于诊断滚子轴承故障。首先，通过使用局部均值分解（LMD）方法将原始的调制滚子轴承振动信号分解为乘积函数（PFs）。其次，将包含主要故障信息的某些PF的包络谱中不同故障特征频率处的振幅比定义为特征振幅比。最后，将特征振幅比用作ACROA-SVM分类器的输入，并识别出滚动轴承的故障模式。结果表明，这种ACROA-SVM分类器和LMD方法的组合可以有效地提高故障诊断的准确率并减少成本。

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