本文選題：特征提取 + 小波包分解　； 參考：《計算機仿真》2017年05期

【摘要】：研究機電設(shè)備振動信號故障診斷問題,由于運行在復(fù)雜工況下的故障信號很容易淹沒在噪聲中,而傳統(tǒng)的特征提取方法不能較好地對特征信號準確提取,降低了故障診斷的準確性。針對上述問題,提出了基于頻帶方差的小波包分解與參數(shù)優(yōu)化的支持向量機的故障診斷算法。首先利用振動信號的小波包分解系數(shù)計算各子頻帶的方差,并作為特征向量。然后將特征向量作為支持向量機的輸入優(yōu)化其參數(shù),對故障進行"多對多""的分類。仿真結(jié)果表明,提出的算法提高了機電設(shè)備振動信號故障診斷的準確率。
[Abstract]:The problem of fault diagnosis of vibration signal of electromechanical equipment is studied. Because the fault signal running under complex working conditions is easily submerged in noise, the traditional feature extraction method can not extract the characteristic signal accurately. The accuracy of fault diagnosis is reduced. Aiming at the above problems, a fault diagnosis algorithm based on wavelet packet decomposition and parameter optimization for support vector machines based on band variance is proposed. First, the wavelet packet decomposition coefficient of vibration signal is used to calculate the variance of each subband, and it is used as the eigenvector. Then the feature vector is used as the input of the support vector machine to optimize its parameters, and the fault is classified as "many-to-many". Simulation results show that the proposed algorithm improves the accuracy of vibration signal fault diagnosis of electromechanical equipment.
【作者單位】： 北京信息科技大學(xué)信息與通信工程學(xué)院;
【基金】：北京市科技創(chuàng)新提升計劃項目(PXM2016_014224_000021)
【分類號】：TH17

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