本文關(guān)鍵詞：基于相對變換的ICA故障檢測方法　出處：《電子測量與儀器學報》2017年07期 　論文類型：期刊論文

  更多相關(guān)文章： 電主軸 故障檢測 相對變換 歐氏距離 獨立主元分析

【摘要】：針對傳統(tǒng)獨立主元分析方法(independent component analysis,ICA)在標準化處理后導致特征值大小近似相等,難以提取有代表性變量等問題,提出了一種基于相對變換的獨立主元分析(relative transformation ICA,RTICA)故障檢測方法。該方法引入歐氏距離相對變換理論,將原始空間數(shù)據(jù)變換得到相對空間,然后在相對空間進行獨立主元分析,降低相對空間的數(shù)據(jù)維數(shù),使提取的獨立主元特征具有更大的適應(yīng)性,建立故障檢測模型,最終實現(xiàn)在線故障檢測。該方法通過田納西-伊斯曼過程仿真加以驗證,并應(yīng)用到電主軸裂紋故障的狀態(tài)監(jiān)測中,實驗結(jié)果表明該方法能有效減少獨立主元個數(shù),簡化故障檢測模型的復(fù)雜度,增強狀態(tài)檢測性能。
[Abstract]:In view of the traditional independent component analysis method (independent component analysis, ICA) in the size of approximately equal eigenvalues in the standardized treatment, it is difficult to extract representative variables and other issues, put forward a kind of independent component analysis based on relative transformation (relative transformation ICA, RTICA) fault detection method. The method is based on Euclidean distance relative transform theory, transform the original data to obtain the relative space space, then independent component analysis in relative space, reduce the dimension of the space is relatively independent, the principal component extraction feature has more adaptability, establish the model of fault detection, finally realizes the online fault detection. The method by Tennessee Eastman process simulation examples, and applied to condition monitoring of electric spindle crack fault. The experimental results show that this method can effectively reduce the number of independent component, simplified fault detection The complexity of the model, enhanced state detection performance.

【作者單位】： 沈陽建筑大學國家地方聯(lián)合工程實驗室;
【基金】：沈陽市科技計劃(17-231-1-28) 遼寧省自然科學基金(2016010623) 中國博士后科學基金(2016M601335)資助項目
【分類號】：TG659
【正文快照】： 1引言隨著高速加工技術(shù)的不斷進步和發(fā)展,尤其是在航空、航天、汽車、輪船等高端技術(shù)行業(yè)的廣泛應(yīng)用,以及機械、電子等產(chǎn)品的需求不斷增加,使得數(shù)控機床技術(shù)越來越受到重視。數(shù)控機床是將高效率、高精度以及高柔性集為一體,不僅提高精度,而且提高生產(chǎn)效率,而高速電主軸又是數(shù)，

本文編號：1411875