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Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network

The quality of rolling bearings is vital for the working state and rotation accuracy of the shaft. Timely and accurately acquiring bearing status and early fault diagnosis can effectively prevent losses, making it highly practical. To improve the accuracy of bearing fault diagnosis, this paper propo...

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Detalles Bibliográficos
Autores principales: Tian, He, Fan, Huaicong, Feng, Mingwen, Cao, Ranran, Li, Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385623/
https://www.ncbi.nlm.nih.gov/pubmed/37514802
http://dx.doi.org/10.3390/s23146508
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author Tian, He
Fan, Huaicong
Feng, Mingwen
Cao, Ranran
Li, Dong
author_facet Tian, He
Fan, Huaicong
Feng, Mingwen
Cao, Ranran
Li, Dong
author_sort Tian, He
collection PubMed
description The quality of rolling bearings is vital for the working state and rotation accuracy of the shaft. Timely and accurately acquiring bearing status and early fault diagnosis can effectively prevent losses, making it highly practical. To improve the accuracy of bearing fault diagnosis, this paper proposes a CNN-LSTM bearing fault diagnosis model optimized by hybrid particle swarm optimization (HPSO). The HPSO algorithm has a strong global optimization ability and can effectively solve nonlinear and multivariate optimization problems. It is used to optimize and match the parameters of the CNN-LSTM model and dynamically find the optimal value of the parameters. This model overcomes the problem that the parameters of the CNN-LSTM model depend on empirical settings and cannot be adjusted dynamically. This model is used for bearing fault diagnosis, and the accuracy rate of fault diagnosis classification reaches 99.2%. Compared with the traditional CNN, LSTM, and CNN-LSTM models, the accuracy rates are increased by 6.6%, 9.2%, and 5%, respectively. At the same time, comparing the models with different optimization parameters shows that the model proposed in this paper has the highest accuracy. The experimental results verified the superiority of the HPSO algorithm to optimize model parameters and the feasibility and accuracy of the HPSO-CNN-LSTM model for bearing fault diagnosis.
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spelling pubmed-103856232023-07-30 Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network Tian, He Fan, Huaicong Feng, Mingwen Cao, Ranran Li, Dong Sensors (Basel) Article The quality of rolling bearings is vital for the working state and rotation accuracy of the shaft. Timely and accurately acquiring bearing status and early fault diagnosis can effectively prevent losses, making it highly practical. To improve the accuracy of bearing fault diagnosis, this paper proposes a CNN-LSTM bearing fault diagnosis model optimized by hybrid particle swarm optimization (HPSO). The HPSO algorithm has a strong global optimization ability and can effectively solve nonlinear and multivariate optimization problems. It is used to optimize and match the parameters of the CNN-LSTM model and dynamically find the optimal value of the parameters. This model overcomes the problem that the parameters of the CNN-LSTM model depend on empirical settings and cannot be adjusted dynamically. This model is used for bearing fault diagnosis, and the accuracy rate of fault diagnosis classification reaches 99.2%. Compared with the traditional CNN, LSTM, and CNN-LSTM models, the accuracy rates are increased by 6.6%, 9.2%, and 5%, respectively. At the same time, comparing the models with different optimization parameters shows that the model proposed in this paper has the highest accuracy. The experimental results verified the superiority of the HPSO algorithm to optimize model parameters and the feasibility and accuracy of the HPSO-CNN-LSTM model for bearing fault diagnosis. MDPI 2023-07-19 /pmc/articles/PMC10385623/ /pubmed/37514802 http://dx.doi.org/10.3390/s23146508 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tian, He
Fan, Huaicong
Feng, Mingwen
Cao, Ranran
Li, Dong
Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network
title Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network
title_full Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network
title_fullStr Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network
title_full_unstemmed Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network
title_short Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network
title_sort fault diagnosis of rolling bearing based on hpso algorithm optimized cnn-lstm neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385623/
https://www.ncbi.nlm.nih.gov/pubmed/37514802
http://dx.doi.org/10.3390/s23146508
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AT caoranran faultdiagnosisofrollingbearingbasedonhpsoalgorithmoptimizedcnnlstmneuralnetwork
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