RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON IMPROVED HHO-LSTM
In order to solve the problem of rolling bearing fault diagnosis, an intelligent diagnosis model IHHO-LSTM was proposed, which combined the improved Harris hawks optimization (HHO) algorithm with long short-term memory (LSTM) network. HHO algorithm was prone to fall into local optimum and slow conve...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2024-02-01
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| Series: | Jixie qiangdu |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.01.003 |
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| _version_ | 1841534207141085184 |
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| author | SHAO LiangShan ZHU SiJia |
| author_facet | SHAO LiangShan ZHU SiJia |
| author_sort | SHAO LiangShan |
| collection | DOAJ |
| description | In order to solve the problem of rolling bearing fault diagnosis, an intelligent diagnosis model IHHO-LSTM was proposed, which combined the improved Harris hawks optimization (HHO) algorithm with long short-term memory (LSTM) network. HHO algorithm was prone to fall into local optimum and slow convergence in the solution process. Based on thesc problems, Cauchy distribution function and simulated annealing (SA) algorithm were introduced to expand the universality of global search and avoid falling into local optimization. The improved HHO was used to quickly determine the optimal super parameter values of LSTM model, so as to improve the accuracy of time series diagnosis. The rolling bearing experimental data of Case Western Reserve University were used for fault diagnosis experiments. The results show that IHHO-LSTM model can realize the feature extraction and fault diagnosis of rolling bearing, and the accuracy of the model is nearly 97%. |
| format | Article |
| id | doaj-art-d81ffb8a7523459eb5eff448e6dd58f9 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2024-02-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-d81ffb8a7523459eb5eff448e6dd58f92025-01-15T02:44:51ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-02-0146172355274187RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON IMPROVED HHO-LSTMSHAO LiangShanZHU SiJiaIn order to solve the problem of rolling bearing fault diagnosis, an intelligent diagnosis model IHHO-LSTM was proposed, which combined the improved Harris hawks optimization (HHO) algorithm with long short-term memory (LSTM) network. HHO algorithm was prone to fall into local optimum and slow convergence in the solution process. Based on thesc problems, Cauchy distribution function and simulated annealing (SA) algorithm were introduced to expand the universality of global search and avoid falling into local optimization. The improved HHO was used to quickly determine the optimal super parameter values of LSTM model, so as to improve the accuracy of time series diagnosis. The rolling bearing experimental data of Case Western Reserve University were used for fault diagnosis experiments. The results show that IHHO-LSTM model can realize the feature extraction and fault diagnosis of rolling bearing, and the accuracy of the model is nearly 97%.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.01.003 |
| spellingShingle | SHAO LiangShan ZHU SiJia RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON IMPROVED HHO-LSTM Jixie qiangdu |
| title | RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON IMPROVED HHO-LSTM |
| title_full | RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON IMPROVED HHO-LSTM |
| title_fullStr | RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON IMPROVED HHO-LSTM |
| title_full_unstemmed | RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON IMPROVED HHO-LSTM |
| title_short | RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON IMPROVED HHO-LSTM |
| title_sort | research on rolling bearing fault diagnosis based on improved hho lstm |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.01.003 |
| work_keys_str_mv | AT shaoliangshan researchonrollingbearingfaultdiagnosisbasedonimprovedhholstm AT zhusijia researchonrollingbearingfaultdiagnosisbasedonimprovedhholstm |