Loss ratio of models.
<div><p>Motor rolling bearing is a fundamental component of industrial production, and its vibration signal extraction and fault diagnosis are challenging because of the effect of operating characteristics and external noise. This research initially proposes an adaptive variational mode...
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2025
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| _version_ | 1852014239274762240 |
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| author | Xianlin Ren (22783589) |
| author2 | Haowen Li (4479226) Laixian Chen (22783592) Siyao Xiong (22783595) Zhengwen Li (685587) |
| author2_role | author author author author |
| author_facet | Xianlin Ren (22783589) Haowen Li (4479226) Laixian Chen (22783592) Siyao Xiong (22783595) Zhengwen Li (685587) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xianlin Ren (22783589) Haowen Li (4479226) Laixian Chen (22783592) Siyao Xiong (22783595) Zhengwen Li (685587) |
| dc.date.none.fl_str_mv | 2025-12-04T18:32:43Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0337832.g006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Loss_ratio_of_models_/30791456 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified vibration signal extraction stacked denoising auto sine cosine algorithm research initially proposes high prediction accuracy finish feature extraction create feature vectors cauchy chaotic mutation enhanced inertia weights fault diagnosis model noise resistance compared fault diagnosis enhanced vmd strong noise external noise tanimoto coefficient stronger adaptability permutation entropy operating characteristics industrial production fundamental component fitness function extract signals experimental results encoders network comprehensively evaluate |
| dc.title.none.fl_str_mv | Loss ratio of models. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Motor rolling bearing is a fundamental component of industrial production, and its vibration signal extraction and fault diagnosis are challenging because of the effect of operating characteristics and external noise. This research initially proposes an adaptive variational mode decomposition approach based on dung beetle optimization algorithm to decompose and extract signals. At the same time, a composite optimization indicator function based on Tanimoto coefficient, permutation entropy and kurtosis are presented as the fitness function of decomposition to increase the flexibility and robustness of the technique. Next it combines with composite multiscale permutation entropy to finish feature extraction and create feature vectors. Finally, an enhanced inertia weights and Cauchy chaotic mutation-Sine Cosine Algorithm is utilized to optimize the hyperparameters of the stacked denoising auto-encoders network and construct a fault diagnosis model. The CWRU open bearing dataset is used to comprehensively evaluate the performance of the method, and the experimental results will be compared to show that the method proposed in this paper can effectively extract signal features in the situation of strong noise, while ensuring a high prediction accuracy, and has stronger adaptability and noise resistance compared with other methods.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_2d364ef147b266cbd47e11eb0ec98a8a |
| identifier_str_mv | 10.1371/journal.pone.0337832.g006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30791456 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Loss ratio of models.Xianlin Ren (22783589)Haowen Li (4479226)Laixian Chen (22783592)Siyao Xiong (22783595)Zhengwen Li (685587)Biological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedvibration signal extractionstacked denoising autosine cosine algorithmresearch initially proposeshigh prediction accuracyfinish feature extractioncreate feature vectorscauchy chaotic mutationenhanced inertia weightsfault diagnosis modelnoise resistance comparedfault diagnosisenhanced vmdstrong noiseexternal noisetanimoto coefficientstronger adaptabilitypermutation entropyoperating characteristicsindustrial productionfundamental componentfitness functionextract signalsexperimental resultsencoders networkcomprehensively evaluate<div><p>Motor rolling bearing is a fundamental component of industrial production, and its vibration signal extraction and fault diagnosis are challenging because of the effect of operating characteristics and external noise. This research initially proposes an adaptive variational mode decomposition approach based on dung beetle optimization algorithm to decompose and extract signals. At the same time, a composite optimization indicator function based on Tanimoto coefficient, permutation entropy and kurtosis are presented as the fitness function of decomposition to increase the flexibility and robustness of the technique. Next it combines with composite multiscale permutation entropy to finish feature extraction and create feature vectors. Finally, an enhanced inertia weights and Cauchy chaotic mutation-Sine Cosine Algorithm is utilized to optimize the hyperparameters of the stacked denoising auto-encoders network and construct a fault diagnosis model. The CWRU open bearing dataset is used to comprehensively evaluate the performance of the method, and the experimental results will be compared to show that the method proposed in this paper can effectively extract signal features in the situation of strong noise, while ensuring a high prediction accuracy, and has stronger adaptability and noise resistance compared with other methods.</p></div>2025-12-04T18:32:43ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0337832.g006https://figshare.com/articles/figure/Loss_ratio_of_models_/30791456CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/307914562025-12-04T18:32:43Z |
| spellingShingle | Loss ratio of models. Xianlin Ren (22783589) Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified vibration signal extraction stacked denoising auto sine cosine algorithm research initially proposes high prediction accuracy finish feature extraction create feature vectors cauchy chaotic mutation enhanced inertia weights fault diagnosis model noise resistance compared fault diagnosis enhanced vmd strong noise external noise tanimoto coefficient stronger adaptability permutation entropy operating characteristics industrial production fundamental component fitness function extract signals experimental results encoders network comprehensively evaluate |
| status_str | publishedVersion |
| title | Loss ratio of models. |
| title_full | Loss ratio of models. |
| title_fullStr | Loss ratio of models. |
| title_full_unstemmed | Loss ratio of models. |
| title_short | Loss ratio of models. |
| title_sort | Loss ratio of models. |
| topic | Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified vibration signal extraction stacked denoising auto sine cosine algorithm research initially proposes high prediction accuracy finish feature extraction create feature vectors cauchy chaotic mutation enhanced inertia weights fault diagnosis model noise resistance compared fault diagnosis enhanced vmd strong noise external noise tanimoto coefficient stronger adaptability permutation entropy operating characteristics industrial production fundamental component fitness function extract signals experimental results encoders network comprehensively evaluate |