發(fā)布時間：2018-10-30 19:23

【摘要】：為解決傳統(tǒng)異步電機故障診斷方法因電機結構復雜、信號非平穩(wěn)和機械大數(shù)據(jù)等因素引起的診斷困難問題,提出一種高效準確的異步電機故障診斷(SDAE)方法。該方法利用堆疊降噪自編碼提取信號特征,結合Softmax分類器實現(xiàn)高效準確的電機故障診斷。首先,采集異步電機的整體電流和振動信號,將電流信號與傅里葉變換后的振動頻域信號組合構成樣本,并做歸一化處理;然后,構建堆疊降噪自編碼網(wǎng)絡,確定網(wǎng)絡層數(shù)、各隱藏層節(jié)點數(shù)、學習率等參數(shù);最后,輸入訓練樣本依次訓練自編碼和分類器,微調整個網(wǎng)絡并用測試數(shù)據(jù)驗證網(wǎng)絡的優(yōu)劣。試驗結果表明,在合適的參數(shù)下采用SDAE方法的異步故障診斷準確率高達99.86%,比傳統(tǒng)電機故障診斷方法提升至少6%。
[Abstract]:In order to solve the problem of fault diagnosis of asynchronous motor caused by complex motor structure, non-stationary signal and mechanical big data, an efficient and accurate (SDAE) method for fault diagnosis of asynchronous motor is proposed. In this method, stacking noise reduction and self-coding are used to extract signal features and Softmax classifier is used to realize efficient and accurate motor fault diagnosis. Firstly, the whole current and vibration signal of asynchronous motor are collected, and the current signal is combined with the vibration frequency domain signal after Fourier transform to form a sample, and the signal is normalized. Then, the stacking noise reduction self-coding network is constructed to determine the number of network layers, the number of nodes in each hidden layer, the learning rate and other parameters. Finally, input training samples to train self-coding and classifier in turn, fine-tune the entire network and test data to verify the advantages and disadvantages of the network. The experimental results show that the accuracy of asynchronous fault diagnosis using SDAE method is as high as 99.86 under suitable parameters, which is at least 6 times higher than that of traditional motor fault diagnosis method.
【作者單位】： 南京信息工程大學信息與控制學院;南京信息工程大學計算機與軟件學院;
【基金】：國家自然科學基金資助項目(51405241,51505234)
【分類號】：TM343

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