本文選題：故障診斷 + 數(shù)據(jù)降維��； 參考：《中國機械工程》2017年21期

【摘要】：針對轉(zhuǎn)子故障特征數(shù)據(jù)集降維問題,提出一種基于Schur分解和正交鄰域保持嵌入算法的故障數(shù)據(jù)集降維方法——Schur-ONPE降維方法。該方法首先應(yīng)用小波包分解提取不同頻帶內(nèi)的能量以組成故障特征值集合,然后運用Schur分解和ONPE算法將高維特征集向低維投影,使降維后類內(nèi)散度最小化及類間分離度最大化,最后將降維后得到的低維特征集輸入K近鄰分類器進行模式識別。通過雙跨轉(zhuǎn)子試驗臺的故障特征數(shù)據(jù)集進行驗證,結(jié)果表明該方法能夠有效地解決轉(zhuǎn)子故障特征集的降維問題。
[Abstract]:In order to reduce the dimension of rotor fault feature data set, a fault data set reduction method, Schur-ONPE, is proposed based on Schur decomposition and orthogonal neighborhood preserving embedding algorithm. Firstly, the wavelet packet decomposition is used to extract the energy in different frequency bands to form the fault eigenvalue set. Then, Schur decomposition and ONPE algorithm are used to project the high Viterbi set to the lower dimension, which minimizes the intra-class divergence and maximizes the inter-class separation after dimensionality reduction. Finally, the reduced dimension low-Viterbi gather input K-nearest neighbor classifier is used for pattern recognition. The results show that this method can effectively solve the problem of reducing the dimension of rotor fault feature set.
【作者單位】： 蘭州理工大學(xué)機電工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(51675253)
【分類號】：TH17;TP18

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1 袁德強;基于LLTSA算法的轉(zhuǎn)子故障特征數(shù)據(jù)集降維方法研究[D];蘭州理工大學(xué);2014年

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本文編號：2005631