本文選題：核主元分析 + 支持向量機(jī)��； 參考：《振動(dòng).測試與診斷》2014年04期

【摘要】：針對(duì)異步電機(jī)故障振動(dòng)信號(hào)具有較強(qiáng)的非線性特征,而傳統(tǒng)的線性分析方法易造成振動(dòng)信號(hào)非線性成分的丟失這一情況,提出一種核主元分析和粒子群支持向量機(jī)相結(jié)合的異步電機(jī)故障診斷方法。利用核函數(shù)實(shí)現(xiàn)輸入空間到高維特征空間的非線性映射以及對(duì)映射數(shù)據(jù)的主元分析,得到原始樣本的非線性主元,實(shí)現(xiàn)特征提取和數(shù)據(jù)壓縮,將獲得的核主元特征通過支持向量機(jī)進(jìn)行模式識(shí)別。采用距離比值法和粒子群算法分別對(duì)核主元分析和支持向量機(jī)的參數(shù)進(jìn)行雙重優(yōu)化選擇。實(shí)驗(yàn)結(jié)果表明,該方法能有效提取故障信號(hào)的非線性特征,具有較強(qiáng)的非線性模式識(shí)別能力,相比主元分析和支持向量機(jī)方法,分類效果更好,實(shí)時(shí)性更強(qiáng),可快速有效實(shí)現(xiàn)異步電機(jī)故障診斷。
[Abstract]:The fault vibration signal of asynchronous motor has strong nonlinear characteristics, but the traditional linear analysis method is easy to cause the loss of the nonlinear component of the vibration signal. A fault diagnosis method for asynchronous motor based on kernel principal component analysis (KPCA) and particle swarm support vector machine (PSO) is proposed. Using kernel function to realize nonlinear mapping from input space to high dimensional feature space and principal component analysis of mapping data, the nonlinear principal components of original samples are obtained, and feature extraction and data compression are realized. The kernel principal feature is recognized by support vector machine (SVM). The distance ratio method and particle swarm optimization algorithm are used to select the parameters of kernel principal component analysis and support vector machine respectively. The experimental results show that this method can extract the nonlinear features of fault signals effectively and has a strong nonlinear pattern recognition ability. Compared with principal component analysis and support vector machine, the classification effect is better and the real time is better. The fault diagnosis of asynchronous motor can be realized quickly and effectively.
【作者單位】： 湖南科技大學(xué)機(jī)械設(shè)備健康維護(hù)省重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家高技術(shù)研究發(fā)展計(jì)劃(“八六三”計(jì)劃)資助項(xiàng)目(2013AA041105) 國家自然科學(xué)基金資助項(xiàng)目(51105138) 湖南省教育廳資助項(xiàng)目(11A034) 湖南省科技計(jì)劃資助項(xiàng)目(2012GK3100) 湖南省高校科技創(chuàng)新團(tuán)隊(duì)支持計(jì)劃資助項(xiàng)目
【分類號(hào)】：TM343

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