作　　者： ; ;

机构地区： 中南大学机电工程学院

出　　处： 《中南大学学报（自然科学版）》 2005年第1期97-101,共5页

摘　　要： 针对目前机械故障诊断中难以获得大量故障数据样本以及特征提取和诊断知识获取困难等不足,提出了应用支持向量机进行机械故障诊断的方法,研究了将小波包分析与信号能量分解用于机械故障的特征提取。该方法将振动信号小波包分析后的信号频带能量作为特征向量,输入到由多个支持向量机构成的多故障分类器中进行故障识别和分类。该分类器只需少量训练样本,而且不必预先知道故障分类的经验知识就能实现正确分类。研究结果表明:选用不同核函数及其参数的多故障分类器对分类精度有影响;在样本不带噪声和带15%噪声情况下,支持向量机的分类精度均高于BP神经网络的分类精度,具有更好的分类性能。 Aiming at the difficulty in getting adequate fault samples, extracting eigenvectors and acquiring diagnosis knowledge in machinery fault diagnosis, a novel method for machinery fault diagnosis based on support vector machine (SVM) was put forward, in which wavelet packet analysis and signal energy decomposition were used for the feature extraction machinery faults. According to the method, the energy of different frequency bands after wavelet packet decomposition constitutes the input vectors of support vector machine as eigenvectors, and these eigenvectors were input into multiple fault classifiers to identify faults. The new method, by which multiple faults can be diagnosed, only requires a small quantity of fault samples and it doesn't need the field knowledge of fault diagnosis. The experimental results show that different SVM classifiers, in which different kernel functions and their parameters are adopted, will influence the precision of fault classifiers. Under the circumstances that samples don't include noise signal and samples include 15% noise signal, the classification precision of SVM classifiers is higher than that of BP artificial neural networks. Thus SVM classifiers show better classification performance.

关 键 词： 支持向量机 小波包分析 特征提取 故障诊断 多故障分类器

领　　域： [自动化与计算机技术] [自动化与计算机技术]