Gear Fault Diagnosis Based on VMD Sample Entropy and Discrete Hopfield Neural Network

Jiakai Ding,Dongming Xiao,Liangpei Huang,Xuejun Li,Antonio Forcina

doi:10.1155/2020/8882653

Abstract

The gear fault signal has some defects such as nonstationary nonlinearity. In order to increase the operating life of the gear, the gear operation is monitored. A gear fault diagnosis method based on variational mode decomposition (VMD) sample entropy and discrete Hopfield neural network (DHNN) is proposed. Firstly, the optimal VMD decomposition number is selected by the instantaneous frequency mean value. Then, the sample entropy value of each intrinsic mode function (IMF) is extracted to form the gear feature vectors. The gear feature vectors are coded and used as the memory prototype and memory starting point of DHNN, respectively. Finally, the coding vector is input into DHNN to realize fault pattern recognition. The newly defined coding rules have a significant impact on the accuracy of gear fault diagnosis. Driven by self-associative memory, the coding of gear fault is accurately classified by DHNN. The superiority of the VMD-DHNN method in gear fault diagnosis is verified by comparing with an advanced signal processing algorithm. The results show that the accuracy based on VMD sample entropy and DHNN is 91.67% of the gear fault diagnosis method. The experimental results show that the VMD method is better than the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and empirical mode decomposition (EMD), and the effect of it in the diagnosis of gear fault diagnosis is emphasized.

Highlights

Gears are widely used in modern industrial machines and play a key role
A variational mode decomposition method based on a cuckoo search algorithm to adjust the changes in internal parameters in VMD decomposition is utilized by Yan and Jia [15], and the multicomponent signal could be adaptively decomposed into a subsignal superposition of inherent mode function. e VMD adaptive decomposition algorithm [16] can be realized by adaptively adjusting the parameters of vibration signals of rotating machinery under VMD decomposition, such as the optimal number of mode decomposition and frequency bandwidth control
The vibration signals of nonstationary gear fault are decomposed by the VMD, and their center frequencies are accurately separated. en, each decomposed intrinsic mode function (IMF) is an extracted sample entropy value, and the extracted feature value is formed into the feature vector

Summary

Introduction

Gears are widely used in modern industrial machines and play a key role. When the gear is damaged, the transmission machinery will cause huge economic losses. e corresponding vibration signal will be generated when the gear runs under normal, wear, cracked, and broken teeth, which contains abundant fault information [1]. erefore, it plays an important role to monitor the running state of the gear, which can be detected and replaced when the early weak fault occurs. Rafiee et al [8] introduced an automatic feature extraction system for gear and bearing fault diagnosis using wavelet-based signal processing. A variational mode decomposition method based on a cuckoo search algorithm to adjust the changes in internal parameters in VMD decomposition is utilized by Yan and Jia [15], and the multicomponent signal could be adaptively decomposed into a subsignal superposition of inherent mode function. Erefore, according to the advantages of simple sample entropy calculation and fast calculation speed, this paper selects it to extract the vibration signal feature of gear fault. Based on the shortcomings of the EMD and the LMD, the optimized VMD is utilized to decompose the gear fault vibration signal. In order to quickly and accurately diagnose faults, a gear fault diagnosis based on VMD sample entropy and DHNN is proposed

Experimental System and Methods

Gear Fault Feature Extraction of VMD Sample Entropy

DHNN Fault Diagnosis Model

State of the gear G1 G2 G3 G4

Findings

Conclusions