基于變分模態(tài)分解和排列熵的滾動軸承故障診斷
發(fā)布時間：2018-12-12 13:19
【摘要】：滾動軸承早期故障信號特征微弱且難以提取,為了從軸承振動信號中提取特征參數(shù)用于軸承故障診斷和識別,提出基于變分模態(tài)分解(Variational Mode Decomposition,VMD)和排列熵(Permutation Entropy,PE)的信號特征提取方法,并采用支持向量機(Support Vector Machine,SVM)進行故障識別。對軸承振動信號進行變分模態(tài)分解,得到不同尺度的本征模態(tài)函數(shù);計算各本征模態(tài)函數(shù)的排列熵,組成多尺度的復雜性度量特征向量;將高維特征向量輸入基于支持向量基建立的分類器進行故障識別分類。通過滾動軸承實驗數(shù)據(jù)分析了算法中參數(shù)選取問題,將該方法應(yīng)用于滾動軸承實驗數(shù)據(jù),并與集合經(jīng)驗?zāi)B(tài)分解和小波包分解進行對比,分析結(jié)果表明,基于變分模態(tài)分解和排列熵的診斷方法有更高的診斷準確率,能夠有效實現(xiàn)滾動軸承的故障診斷。
[Abstract]:The early fault signals of rolling bearings are weak and difficult to extract. In order to extract characteristic parameters from bearing vibration signals for bearing fault diagnosis and identification, a new method based on variational mode decomposition (Variational Mode Decomposition,VMD) and permutation entropy (Permutation Entropy,) is proposed. PE) and support vector machine (Support Vector Machine,SVM) for fault identification. Based on the variational mode decomposition of bearing vibration signal, the eigenmode functions of different scales are obtained, the permutation entropy of each intrinsic mode function is calculated, and the complexity metric eigenvector of multiple scales is formed. The high dimensional feature vector is input into the classifier based on support vector basis for fault identification and classification. Based on the experimental data of rolling bearing, the problem of parameter selection in the algorithm is analyzed. The method is applied to the experimental data of rolling bearing, and compared with the empirical mode decomposition and wavelet packet decomposition. The analysis results show that, The diagnosis method based on variational mode decomposition and permutation entropy has higher diagnostic accuracy and can effectively realize the fault diagnosis of rolling bearings.
【作者單位】： 上海電力學院自動化工程學院;上海東海風力發(fā)電有限公司;
【基金】：國家自然科學基金(51507098) 上海綠色能源并網(wǎng)工程技術(shù)研究中心(13DZ2251900) 上海市科委重點科技攻關(guān)項目(14DZ1200905) 上海市電站自動化技術(shù)重點實驗室項目(13DZ2273800)
【分類號】：TH133.33
本文編號：2374637
[Abstract]:The early fault signals of rolling bearings are weak and difficult to extract. In order to extract characteristic parameters from bearing vibration signals for bearing fault diagnosis and identification, a new method based on variational mode decomposition (Variational Mode Decomposition,VMD) and permutation entropy (Permutation Entropy,) is proposed. PE) and support vector machine (Support Vector Machine,SVM) for fault identification. Based on the variational mode decomposition of bearing vibration signal, the eigenmode functions of different scales are obtained, the permutation entropy of each intrinsic mode function is calculated, and the complexity metric eigenvector of multiple scales is formed. The high dimensional feature vector is input into the classifier based on support vector basis for fault identification and classification. Based on the experimental data of rolling bearing, the problem of parameter selection in the algorithm is analyzed. The method is applied to the experimental data of rolling bearing, and compared with the empirical mode decomposition and wavelet packet decomposition. The analysis results show that, The diagnosis method based on variational mode decomposition and permutation entropy has higher diagnostic accuracy and can effectively realize the fault diagnosis of rolling bearings.
【作者單位】： 上海電力學院自動化工程學院;上海東海風力發(fā)電有限公司;
【基金】：國家自然科學基金(51507098) 上海綠色能源并網(wǎng)工程技術(shù)研究中心(13DZ2251900) 上海市科委重點科技攻關(guān)項目(14DZ1200905) 上海市電站自動化技術(shù)重點實驗室項目(13DZ2273800)
【分類號】：TH133.33
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