【摘要】 为解决旋转机械振动信号复杂且难以提取有效故障特征的问题,提出了一种经验模态分解(empirical mode decomposition,简称EMD)、奇异值分解(singular value decomposition,简称SVD)和深度卷积网络(Convolutional Neural Network,简称CNN)相结合的故障诊断方法。首先,通过EMD方法将故障信号分解成若干个固有模态分量(intrinsic mode function,简称IMF),构造时域与频域空间状态矩阵;其次,利用SVD方法对空间状态矩阵进行分解得到奇异值数组,构造时域与频域奇异值特征矩阵;最后,将提取的奇异值特征矩阵输入到CNN中进行模式识别。将该方法分别应用于滚动轴承与齿轮箱故障诊断中,在西储大学滚动轴承数据集、PHM2009直齿齿轮箱数据集上均取得了很好效果,正确率优于将原始信号直接输入到CNN中等几种对比方法,验证了该方法的优越性。更多还原

【Abstract】 In order to extract effective features of complex signals,a fault diagnosis method based on empirical mode decomposition(EMD),singular value decomposition(SVD)and convolutional neural network(CNN)is proposed.First,the fault signal is decomposed into several intrinsic mode function(IMF)components by EMD.A time-domain and a frequency-domain spatial state matrix are constructed.Then,the matrix is decomposed to obtain an array of singular values by SVD,constructing a time-domain and frequency-domain singular value feature matrix.Finally,the extracted singular value feature matrix is input into CNN for pattern recognition.The method is applied to the fault diagnosis of rolling bearing and gearbox,and has achieved good results in the data of the Case Western Reserve University and the PHM2009 dataset.The correct rate is better than the direct comparison of the original signal into CNN,which verifies the superiority of the method.更多还原