Gearbox Fault Diagnosis based on LMD Approximate Entropy and PSO-ELM

For the detection and identification problems of common faults in the use of gearbox,considering the nonlinear,non-stationary properties of the gearbox vibration response signal,a method for the gearbox fault diagnosis based on local mean decomposition( LMD) approximate entropy and PSO-ELM is propos...

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Main Authors: Zhang Yuxue, Pan Hongxia
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Transmission 2017-01-01
Series:Jixie chuandong
Subjects:
Online Access:http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.08.023
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author Zhang Yuxue
Pan Hongxia
author_facet Zhang Yuxue
Pan Hongxia
author_sort Zhang Yuxue
collection DOAJ
description For the detection and identification problems of common faults in the use of gearbox,considering the nonlinear,non-stationary properties of the gearbox vibration response signal,a method for the gearbox fault diagnosis based on local mean decomposition( LMD) approximate entropy and PSO-ELM is proposed.Firstly,the LMD decomposition method is used for gearbox vibration signal,with correlation coefficient method extracted the first four PF components which contain the main fault information. By using the approximate entropy to describe quantitatively,and the feature vector is formed. Finally,the input weights of ELM and the threshold value of the hidden layer neurons are optimized by the particle swarm optimization algorithm,the model of PSO-ELM is established,and the approximate entropy values are input into the ELM and PSO-ELM models to recognize and classify the fault types of the gearbox of different conditions. The results show that based on LMD approximate entropy and PSO-ELM has the higher classification accuracy,the feasibility of this method is verified.
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spelling doaj.art-160811889547442d83b45aff99b8919e2023-05-26T09:45:29ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392017-01-014110911329931708Gearbox Fault Diagnosis based on LMD Approximate Entropy and PSO-ELMZhang YuxuePan HongxiaFor the detection and identification problems of common faults in the use of gearbox,considering the nonlinear,non-stationary properties of the gearbox vibration response signal,a method for the gearbox fault diagnosis based on local mean decomposition( LMD) approximate entropy and PSO-ELM is proposed.Firstly,the LMD decomposition method is used for gearbox vibration signal,with correlation coefficient method extracted the first four PF components which contain the main fault information. By using the approximate entropy to describe quantitatively,and the feature vector is formed. Finally,the input weights of ELM and the threshold value of the hidden layer neurons are optimized by the particle swarm optimization algorithm,the model of PSO-ELM is established,and the approximate entropy values are input into the ELM and PSO-ELM models to recognize and classify the fault types of the gearbox of different conditions. The results show that based on LMD approximate entropy and PSO-ELM has the higher classification accuracy,the feasibility of this method is verified.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.08.023Gearbox;LMD;Approximate entropy;PSO-ELM;Fault diagnosis
spellingShingle Zhang Yuxue
Pan Hongxia
Gearbox Fault Diagnosis based on LMD Approximate Entropy and PSO-ELM
Jixie chuandong
Gearbox;LMD;Approximate entropy;PSO-ELM;Fault diagnosis
title Gearbox Fault Diagnosis based on LMD Approximate Entropy and PSO-ELM
title_full Gearbox Fault Diagnosis based on LMD Approximate Entropy and PSO-ELM
title_fullStr Gearbox Fault Diagnosis based on LMD Approximate Entropy and PSO-ELM
title_full_unstemmed Gearbox Fault Diagnosis based on LMD Approximate Entropy and PSO-ELM
title_short Gearbox Fault Diagnosis based on LMD Approximate Entropy and PSO-ELM
title_sort gearbox fault diagnosis based on lmd approximate entropy and pso elm
topic Gearbox;LMD;Approximate entropy;PSO-ELM;Fault diagnosis
url http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.08.023
work_keys_str_mv AT zhangyuxue gearboxfaultdiagnosisbasedonlmdapproximateentropyandpsoelm
AT panhongxia gearboxfaultdiagnosisbasedonlmdapproximateentropyandpsoelm