Fault diagnosis of reciprocating machinery based on improved MEEMD-SqueezeNet

Abstract

Reciprocating machinery structure is complex, and its signal is nonlinear and nonstationary. Because of the complex working environment, the signal usually contains noise interference. Modified Ensemble Empirical Mode Decomposition(MEEMD) reduces residual noise by introducing permutation entropy. But it directly eliminates components larger than the threshold, which may lose the effective information of the original signal. Deep learning models can solve nonlinear problems. But with the number of network layers increases, it increases the number of parameters, resulting in long model training time. Based on the above analysis, this paper proposes a fault diagnosis of reciprocating machinery based on improved MEEMD-SqueezeNet. By introducing multi-scale permutation entropy to replace the original single-scale permutation entropy, the calculated entropy contains more scale information. Singular value decomposition (SVD) is carried out to denoise the signal larger than the threshold to retain more effective information in the original signal. Convert the noise reduced 1D time series to 2D images by Gram Angle Field(GAF) to preserve the temporal correlation of 1D vibration data. Combined with the idea of Inception series model, an InFire module is proposed to replace the original Fire model of SqueezeNet. Use the improved SqueezeNet to extract features from the generated 2D images, and finally input the softmax classifier for classification to obtain fault diagnosis results. The simulation experiment and noise reduction comparison experiment prove that the improved MEEMD method proposed in this paper can effectively achieve signal noise reduction and improve the overall average accuracy of diagnosis from 89.49% to 94.35%. The fault classification comparison experiments prove that the accuracy of improved SqueezeNet is basically the same as that of VGG19. Compared with SqueezeNet, the accuracy of improved Squeezenet is improved by 1.77%, and the number of model parameters is reduced by nearly 29.4%. In terms of training time, it is proved that the training time of the improved SqueezeNet is basically the same as that of SqueezeNet, accounting for only one-third of the training time of VGG19. The improved SqueezeNet not only ensures lower training time, but also improves the accuracy of SqueezeNet. The above experimental results show that the proposed method reduces the model training time and improves the fault diagnosis accuracy of reciprocating machinery.