基于奇异值分解和小波包分解的故障检测

Fault detection method based on SVD and WPD

根据真空泵在故障和正常模式下工作时,其振动信号在频域的能量分布的差异性,设计基于奇异值分解(SVD)和小波包分解(WPD)的真空泵故障检测方法。首先用SVD对采集到的信号进行去噪,再使用小波包对去噪后的信号进行分解,对分解得到的各层系数进行重构并提取需要的各频域段的能量。将提取的能量向量作为支持向量机(SVM)的输入样本,对SVM进行训练。最后使用实验数据对SVM的可靠性进行验证。实验结果表明,采用SVD和WPD结合的方法能较好地识别出真空泵的故障。

A fault detection method based on singular value decomposition（SVD） and wavelet packet decomposition（WPD） is designed.The basis principle of this method is the difference of energy distribution of the vibration signals in the frequency domain when the machine works in the normal mode and the failure mode. Firstly, the signal is de-noised by SVD. Secondly, the signal samples are decomposed by using the wavelet packet, and then the wavelet packet coefficients are reconstructed and the energy of different layers is extracted. The energy extracted from different frequency domain is taken as a feature vector, and it will be the input sample of the support vector machine（SVM）. Finally, the correctness and feasibility of SVM are proved through practical examples. The results of experiment show that the method based on SVD and WPD can identify the faults of the vacuum pump well.