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

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

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 CEEMD...

Full description

Bibliographic Details
Main Authors: Chen Xi-Hui, Cheng Gang, Liu Chang, Li Yong
Format: Article
Language:English
Published: EDP Sciences 2017-01-01
Series:ITM Web of Conferences
Online Access:https://doi.org/10.1051/itmconf/20171108002
id doaj-f16ddcdaa7904b77925a269b5fcd5548
record_format Article
spelling doaj-f16ddcdaa7904b77925a269b5fcd55482021-02-02T02:21:39ZengEDP SciencesITM Web of Conferences2271-20972017-01-01110800210.1051/itmconf/20171108002itmconf_ist2017_08002Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration SignalChen Xi-HuiCheng GangLiu ChangLi YongA 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.https://doi.org/10.1051/itmconf/20171108002
collection DOAJ
language English
format Article
sources DOAJ
author Chen Xi-Hui
Cheng Gang
Liu Chang
Li Yong
spellingShingle Chen Xi-Hui
Cheng Gang
Liu Chang
Li Yong
Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal
ITM Web of Conferences
author_facet Chen Xi-Hui
Cheng Gang
Liu Chang
Li Yong
author_sort Chen Xi-Hui
title Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal
title_short Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal
title_full Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal
title_fullStr Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal
title_full_unstemmed Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal
title_sort fault diagnosis of planetary gear based on fuzzy entropy of ceemdan and mlp neural network by using vibration signal
publisher EDP Sciences
series ITM Web of Conferences
issn 2271-2097
publishDate 2017-01-01
description 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.
url https://doi.org/10.1051/itmconf/20171108002
work_keys_str_mv AT chenxihui faultdiagnosisofplanetarygearbasedonfuzzyentropyofceemdanandmlpneuralnetworkbyusingvibrationsignal
AT chenggang faultdiagnosisofplanetarygearbasedonfuzzyentropyofceemdanandmlpneuralnetworkbyusingvibrationsignal
AT liuchang faultdiagnosisofplanetarygearbasedonfuzzyentropyofceemdanandmlpneuralnetworkbyusingvibrationsignal
AT liyong faultdiagnosisofplanetarygearbasedonfuzzyentropyofceemdanandmlpneuralnetworkbyusingvibrationsignal
_version_ 1724310034937020416

Similar Items