Analysis method combining improved AE algorithm and signal reconstruction in mechanical faults
IntroductionFault diagnosis analysis of mechanical equipment is greatly significant for maintaining the production efficiency of enterprises. Traditional diagnostic methods have shortcomings in accuracy and robustness.MethodsTherefore, the study integrates variational autoencoders with long short-te...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Mechanical Engineering |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmech.2025.1635741/full |
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| _version_ | 1849418717798072320 |
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| author | Zhenhua Niu |
| author_facet | Zhenhua Niu |
| author_sort | Zhenhua Niu |
| collection | DOAJ |
| description | IntroductionFault diagnosis analysis of mechanical equipment is greatly significant for maintaining the production efficiency of enterprises. Traditional diagnostic methods have shortcomings in accuracy and robustness.MethodsTherefore, the study integrates variational autoencoders with long short-term memory network models, enhances them using dropout methods, and proposes a hybrid diagnostic analysis model that combines improved autoencoder algorithms and signal reconstruction.ResultsThe experiment outcomes indicated that under the slow degradation mode of the bearing, the precision, recall, F1 score, and overall accuracy of the improved autoencoder model were 0.931, 0.933, 0.920, and 0.939, respectively, which were better than the pre-modified model. The fault diagnosis results showed that in the rapid degradation mode of the bearing, the research model discovered potential faults at 8,830 s, earlier than other models. The ablation experiment results showed that the precision, recall, F1 score, and overall accuracy of the enhanced study model using the dropout method were 0.83, 0.80, 0.82, and 0.99, respectively. Compared with the baseline model, the four indicators improved by 5.1%, 6.7%, 6.5%, and 5.3%, respectively. The memory usage test findings denoted that the average memory usage of the research model was less than 46%, which was better than the control model.DiscussionThe research promotes innovation and optimization of mechanical fault diagnosis technology, improves the accuracy and timeliness of fault diagnosis analysis models, and is of great significance for ensuring production safety, reducing maintenance costs, and improving enterprise economic benefits. |
| format | Article |
| id | doaj-art-8ed03fbfa00b47969999f9fc87474f45 |
| institution | Kabale University |
| issn | 2297-3079 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Mechanical Engineering |
| spelling | doaj-art-8ed03fbfa00b47969999f9fc87474f452025-08-20T03:32:22ZengFrontiers Media S.A.Frontiers in Mechanical Engineering2297-30792025-07-011110.3389/fmech.2025.16357411635741Analysis method combining improved AE algorithm and signal reconstruction in mechanical faultsZhenhua NiuIntroductionFault diagnosis analysis of mechanical equipment is greatly significant for maintaining the production efficiency of enterprises. Traditional diagnostic methods have shortcomings in accuracy and robustness.MethodsTherefore, the study integrates variational autoencoders with long short-term memory network models, enhances them using dropout methods, and proposes a hybrid diagnostic analysis model that combines improved autoencoder algorithms and signal reconstruction.ResultsThe experiment outcomes indicated that under the slow degradation mode of the bearing, the precision, recall, F1 score, and overall accuracy of the improved autoencoder model were 0.931, 0.933, 0.920, and 0.939, respectively, which were better than the pre-modified model. The fault diagnosis results showed that in the rapid degradation mode of the bearing, the research model discovered potential faults at 8,830 s, earlier than other models. The ablation experiment results showed that the precision, recall, F1 score, and overall accuracy of the enhanced study model using the dropout method were 0.83, 0.80, 0.82, and 0.99, respectively. Compared with the baseline model, the four indicators improved by 5.1%, 6.7%, 6.5%, and 5.3%, respectively. The memory usage test findings denoted that the average memory usage of the research model was less than 46%, which was better than the control model.DiscussionThe research promotes innovation and optimization of mechanical fault diagnosis technology, improves the accuracy and timeliness of fault diagnosis analysis models, and is of great significance for ensuring production safety, reducing maintenance costs, and improving enterprise economic benefits.https://www.frontiersin.org/articles/10.3389/fmech.2025.1635741/fullmechanical equipmentfault diagnosisauto encodersignal reconstructionLSTM |
| spellingShingle | Zhenhua Niu Analysis method combining improved AE algorithm and signal reconstruction in mechanical faults Frontiers in Mechanical Engineering mechanical equipment fault diagnosis auto encoder signal reconstruction LSTM |
| title | Analysis method combining improved AE algorithm and signal reconstruction in mechanical faults |
| title_full | Analysis method combining improved AE algorithm and signal reconstruction in mechanical faults |
| title_fullStr | Analysis method combining improved AE algorithm and signal reconstruction in mechanical faults |
| title_full_unstemmed | Analysis method combining improved AE algorithm and signal reconstruction in mechanical faults |
| title_short | Analysis method combining improved AE algorithm and signal reconstruction in mechanical faults |
| title_sort | analysis method combining improved ae algorithm and signal reconstruction in mechanical faults |
| topic | mechanical equipment fault diagnosis auto encoder signal reconstruction LSTM |
| url | https://www.frontiersin.org/articles/10.3389/fmech.2025.1635741/full |
| work_keys_str_mv | AT zhenhuaniu analysismethodcombiningimprovedaealgorithmandsignalreconstructioninmechanicalfaults |