一种基于优化VMD-CWT-CNN的柱塞泵配流盘磨损状态识别方法

A recognition method of valve plate wear states of piston pump based on optimized VMD-CWT-CNN

为解决一维振动信号难以充分挖掘表达状态特征信息以及柱塞泵配流盘磨损早期识别问题,基于卷积神经网络(Convolutional neural networks,CNN)优秀的图像处理能力,提出了一个优化VMD-CWT-CNN模型。首先,采用连续小波变换(Continuous wavelet transform,CWT)对信号进行预处理,得到信号的二维时频图,作为CNN模型的一路输入,将状态识别问题转化为CNN图像识别问题。其次,基于相关系数对变分模态分解(Variational mode decomposition,VMD)参数优化后,利用优化VMD对振动信号进行预处理,再以相关系数和峭度值最大为优选原则,甄选出三组蕴含故障特征的本征模态函数(Intrinsic mode function,IMF),将其重组为三通道一维信号,作为CNN模型的另一路输入。最后,在CNN模型中将两路信息汇聚并得到柱塞泵配流盘磨损状态识别分类结果。实验中,此方法分别采用优化VMD和CWT对振动信号预处理,再结合CNN对磨损状态进行分类。实验结果表明,该方法对于配流盘磨损的三种状态的识别效果显著优于单路输入的CNN模型以及典型的深度学习方法和机器学习分类器。因此,优化的VMD-CWT-CNN方法可以更准确地实现柱塞泵配流盘磨损状态识别。

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.