Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal

Xi-Hui Chen,Chang Liu,Gang Cheng,Yong Li,Hui Yang

doi:10.1051/itmconf/20171108002

Abstract

A method of planetary gear fault diagnosis based on the fuzzy entropy of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-layer perceptron (MLP) neural network is proposed. The vibration signal is decomposed into multiple intrinsic mode functions (IMFs) by CEEMDAN, and the fuzzy entropy that combines the fuzzy function and sample entropy is proposed and used to extract the feature information contained in each IMF. The fuzzy entropies of each IMF are defined as the input of the MLP neural network, and the planetary gear status can be recognized by the output. The experiments prove the proposed method is effective.

Highlights

Planetary gear often used in the key parts of the transmission system of large-scale complex equipment
A testing sample set that includes different planetary gear status samples is prepared, and each planetary gear status has 40 samples, for a total of 200 samples. They are decomposed by CEEMDAN, and the fuzzy entropies of each intrinsic mode functions (IMFs) are inputted into the trained multi-layer perceptron (MLP) neural network for use in verifying the recognition performance of the trained MLP neural network
A method of planetary gear fault diagnosis based on the fuzzy entropy of CEEMDAN and MLP neural network is proposed

Summary

Introduction

Planetary gear often used in the key parts of the transmission system of large-scale complex equipment It often suffered from the influences of heavy-duty and hostile environment, leading faults to occur [1]. Due to the influence of its complex structure, installation errors and operating environment, the vibration signal of planetary gear shows the characteristics with nonlinear and nonstationary [2]. A fault feature extraction method that is suitable for processing the nonstationary vibration signal should be developed. MLP neural network can be applied to recognize different planetary gear statuses, generating a structured network to achieve highly complex nonlinear mapping by data self-learning [8]. In this paper, a fault diagnosis method of planetary gear based on fuzzy entropy of CEEMDAN and MLP neural network is established

Complete ensemble empirical mode decomposition with adaptive noise

Fuzzy entropy

Multi-Layer Perceptron Neural Network

Test equipment and data acquisition

Experiment signal analysis

Conclusions