Health state assessment method for complex system based on multiexpert joint belief rule base
Abstract The health of complex systems continues to decline as they operate over long periods of time, so it is important to assess the health state of complex systems. Belief rule base (BRB) is widely used in the field of health state assessment of complex systems as a semi-quantitative method that...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85792-8 |
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| author | Shuozi Li Mingyuan Liu Ning Ma Wei He |
| author_facet | Shuozi Li Mingyuan Liu Ning Ma Wei He |
| author_sort | Shuozi Li |
| collection | DOAJ |
| description | Abstract The health of complex systems continues to decline as they operate over long periods of time, so it is important to assess the health state of complex systems. Belief rule base (BRB) is widely used in the field of health state assessment of complex systems as a semi-quantitative method that can address uncertainty effectively and with interpretability. In practical engineering, BRB still has problems: the incompleteness of expert knowledge and the inconsistency of the cognitive abilities of each expert have an effect on the construction of the model and interpretability. To address this problem, a complex system health state assessment method is proposed based on a joint multiexpert belief rule base (BRB-ME). Experts first build their own models, and a new multiexpert knowledge fusion algorithm is designed for the fusion of different expert models. The ER is used as the inference machine for the model. Next, a multi-population evolution whale optimization algorithm with multiexpert knowledge constraints (C-MEWOA) is used to optimize the BRB-ME model. Finally, the effectiveness of the BRB-ME model in health state assessment is verified through case studies of lithium-ion batteries and flywheels. Comparative studies have shown that the BRB-ME model can fuse multiexpert knowledge and has advantages in terms of the stability and accuracy of assessment results. |
| format | Article |
| id | doaj-art-490e533a5be3423181b9c4c89ab943ff |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-490e533a5be3423181b9c4c89ab943ff2025-01-26T12:29:54ZengNature PortfolioScientific Reports2045-23222025-01-0115112310.1038/s41598-025-85792-8Health state assessment method for complex system based on multiexpert joint belief rule baseShuozi Li0Mingyuan Liu1Ning Ma2Wei He3School of Computer Science and Information Engineering, Harbin Normal UniversitySchool of Computer Science and Information Engineering, Harbin Normal UniversitySchool of Computer Science and Information Engineering, Harbin Normal UniversitySchool of Computer Science and Information Engineering, Harbin Normal UniversityAbstract The health of complex systems continues to decline as they operate over long periods of time, so it is important to assess the health state of complex systems. Belief rule base (BRB) is widely used in the field of health state assessment of complex systems as a semi-quantitative method that can address uncertainty effectively and with interpretability. In practical engineering, BRB still has problems: the incompleteness of expert knowledge and the inconsistency of the cognitive abilities of each expert have an effect on the construction of the model and interpretability. To address this problem, a complex system health state assessment method is proposed based on a joint multiexpert belief rule base (BRB-ME). Experts first build their own models, and a new multiexpert knowledge fusion algorithm is designed for the fusion of different expert models. The ER is used as the inference machine for the model. Next, a multi-population evolution whale optimization algorithm with multiexpert knowledge constraints (C-MEWOA) is used to optimize the BRB-ME model. Finally, the effectiveness of the BRB-ME model in health state assessment is verified through case studies of lithium-ion batteries and flywheels. Comparative studies have shown that the BRB-ME model can fuse multiexpert knowledge and has advantages in terms of the stability and accuracy of assessment results.https://doi.org/10.1038/s41598-025-85792-8Health assessmentMultiexpert joint belief rule baseLithium-ion batteryFlywheel |
| spellingShingle | Shuozi Li Mingyuan Liu Ning Ma Wei He Health state assessment method for complex system based on multiexpert joint belief rule base Scientific Reports Health assessment Multiexpert joint belief rule base Lithium-ion battery Flywheel |
| title | Health state assessment method for complex system based on multiexpert joint belief rule base |
| title_full | Health state assessment method for complex system based on multiexpert joint belief rule base |
| title_fullStr | Health state assessment method for complex system based on multiexpert joint belief rule base |
| title_full_unstemmed | Health state assessment method for complex system based on multiexpert joint belief rule base |
| title_short | Health state assessment method for complex system based on multiexpert joint belief rule base |
| title_sort | health state assessment method for complex system based on multiexpert joint belief rule base |
| topic | Health assessment Multiexpert joint belief rule base Lithium-ion battery Flywheel |
| url | https://doi.org/10.1038/s41598-025-85792-8 |
| work_keys_str_mv | AT shuozili healthstateassessmentmethodforcomplexsystembasedonmultiexpertjointbeliefrulebase AT mingyuanliu healthstateassessmentmethodforcomplexsystembasedonmultiexpertjointbeliefrulebase AT ningma healthstateassessmentmethodforcomplexsystembasedonmultiexpertjointbeliefrulebase AT weihe healthstateassessmentmethodforcomplexsystembasedonmultiexpertjointbeliefrulebase |