A recognition method of valve plate wear states of piston pump based on optimized VMD-CWT-CNN
It is difficult to fully mine the information from that one-dimensional vibration signal that expresses the state characteristics and then early recognize the wear of the valve plate of a piston pump. In view of the excellent image processing capabilities of the convolutional neural networks(CNN), we proposed an optimized VMD-CWT-CNN model to solve the above-mentioned problem. Firstly, continuous wavelet transform(CWT) was used to preprocess the signal to obtain a two-dimensional time-frequency diagram of the signal, which iwasused as one input of the CNN model to convert the state recognition problem into a CNN image recognition problem. Secondly, after optimizing variational mode decomposition(VMD) parameters based on correlation coefficient, the vibration signal was preprocessed by using the optimized VMD, and then based on the principle of maximizing the correlation coefficient and the kurtosis value, three groups of Intrinsic mode function(IMF) with fault characteristics were selected and reorganized into a three-channel one-dimensional signal as another input of the CNN model. Finally, in the CNN model, two paths were converged, and the results of the recognition and classification of the valve plate wear states of the piston pump were obtained. In the experiment, the proposed method we first use the optimized VMD and the CWT to preprocess the vibration signal, respectively, and then combined with the CNN to classify the wear states of valve plates. Experimental results show the recognition effect of the proposed method on the three states of valve plate wear is significantly better than that of the single-input CNN model, the typical deep learning method and the machine learning classifier. The optimized VMD-CWT-CNN method can more accurately recognize the valve plate wear states of the piston pump.
万方数据
维普
