为从复合故障信号中提取各故障特征，提出一种离散小波变换（DWT）和约束独立成分分析（CICA）相结合的单通道复合故障诊断方法。首先通过DWT方法将单通道振动信号进行小波分解后，利用小波重构函数重构各层分解信号。然后取重构信号的包络信号作为CICA算法的输入矩阵，基于滚动轴承先验知识建立参考信号，从而分离出轴承各故障信号，提取故障特征。最后，在滚动轴承故障模拟实验台上进行了方法验证。结果表明：该方法可有效分离滚动轴承外圈和滚动体故障，实现了轴承复合故障的诊断。%In order to extract fault features from compound signals, a method based on discrete wavelet transform (DWT) and constrained independent component analysis (CICA) was proposed. In this method, the single channel vibration signal was decomposed into several wavelet coefficients by DWT method, and the wavelet re-construction function was used to reconstruct the decomposed signal. Then, envelope signals of the reconstructed wavelet coefficients were selected as the input matrix of CICA algorithm, and the reference signal was established based on prior knowledge of source signals. Finally, the fault signals were separated and the fault features were extracted. Experimental results validated the effectiveness of the proposed method in compound fault separating and diagnosis of rolling bearings.
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