發(fā)布時間：2018-01-09 20:11

  本文關鍵詞：基于EEMD-KECA的風電機組滾動軸承故障診斷　出處：《太陽能學報》2017年07期 　論文類型：期刊論文

  更多相關文章： 故障診斷 聚合經驗模態(tài)分解 核熵成分分析 能量熵 滾動軸承

【摘要】：針對傳統(tǒng)頻域診斷算法不能充分挖掘出非線性、非平穩(wěn)信號內部本質信息的問題,提出基于聚合經驗模態(tài)分解(EEMD)的復合特征提取和基于核熵成分分析(KECA)的故障自動診斷算法。該方法首先采用EEMD將原始信號分解成若干特征模態(tài)函數(IMF),計算IMF能量和信號的能量熵構建復合特征向量并作為KECA的輸入,之后建立KECA非線性分類器并引入一種新的監(jiān)測統(tǒng)計量——散度測度統(tǒng)計量,實現故障的實時監(jiān)測與自動診斷。采用KECA可實現根據熵值大小進行特征分類,具有較強的非線性處理能力,且不同特征信息之間呈現出顯著的角度差異,易于分類。最后通過實際風電機組滾動軸承應用實例對算法進行驗證,結果表明該方法可有效提取信號中的故障特征,實現對滾動軸承的故障診斷,相比神經網絡分類方法具有更高的識別率。
[Abstract]:To solve the problem that the traditional frequency domain diagnosis algorithm can not fully excavate the internal essential information of nonlinear and non-stationary signals. Composite feature extraction based on polymeric empirical mode decomposition (EEMD) and kernel entropy component analysis (KECA) are proposed. The method firstly decomposes the original signal into several characteristic mode functions by EEMD. The energy entropy of IMF and signal is calculated to construct the compound eigenvector as the input of KECA, and then the nonlinear classifier of KECA is established and a new statistical measure of divergence is introduced. KECA can be used to classify features according to entropy value, which has strong nonlinear processing ability, and there are significant angle differences among different feature information. It is easy to classify. Finally, the algorithm is verified by the actual wind turbine rolling bearing application example. The results show that the method can effectively extract the fault features from the signal and realize the fault diagnosis of the rolling bearing. Compared with the neural network classification method, it has a higher recognition rate.
【作者單位】： 內蒙古工業(yè)大學電力學院;內蒙古北方龍源風力發(fā)電有限責任公司;北京工業(yè)大學電子信息與控制工程學院;
【基金】：國家自然科學基金(61364009;21466026) 內蒙古自然科學基金(2015MS0615) 校級重點項目(X201424)
【分類號】：TH133.3;TM315
【正文快照】： 0引言滾動軸承是旋轉機械的主要部件之一,具有效率高、摩擦阻力小、裝配簡單、易潤滑等優(yōu)點,因此被廣泛應用于風力發(fā)電機傳動鏈系統(tǒng),是該系統(tǒng)中應用最普遍、使用最多,也是最易損傷的部件之一。風電機組傳動鏈中的許多故障都與滾動軸承有密切關系,據統(tǒng)計約30%的機械故障與軸承，

本文編號：1402460