Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm
Abstract Nuclear power turbine fault diagnosis is an important issue in the field of nuclear power safety. The numerous state parameters in the operation and maintenance of nuclear power turbines are collected, forming a complex high‐dimensional feature space. These high‐dimensional feature spaces c...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-09-01
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| Series: | IET Collaborative Intelligent Manufacturing |
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| Online Access: | https://doi.org/10.1049/cim2.12108 |
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| author | Ruirui Zhong Yixiong Feng Puyan Li Xuanyu Wu Ao Guo Ansi Zhang Chuanjiang Li |
| author_facet | Ruirui Zhong Yixiong Feng Puyan Li Xuanyu Wu Ao Guo Ansi Zhang Chuanjiang Li |
| author_sort | Ruirui Zhong |
| collection | DOAJ |
| description | Abstract Nuclear power turbine fault diagnosis is an important issue in the field of nuclear power safety. The numerous state parameters in the operation and maintenance of nuclear power turbines are collected, forming a complex high‐dimensional feature space. These high‐dimensional feature spaces contain redundant information, which increases the training cost and reduces the recognition accuracy and efficiency of the fault diagnosis model. To address the aforementioned challenges, a vibration fault diagnosis algorithm in nuclear power turbines is proposed. First, a long short‐term memory‐based denoising autoencoder (LDAE) is designed to enhance the capability of uncertainty awareness. Then, a feature extraction method integrating variational mode decomposition (VMD), L‐cliffs‐based effective mode selection, and sample entropy is devised to extract the latent features from the complex high‐dimensional feature space. Furthermore, using extreme gradient boosting (XGBoost) as the classifier, LDAE‐VMD‐XGBoost model is constructed for fault diagnosis of nuclear power turbines. Considering the impact of multiple hyperparameters of LDAE‐VMD‐XGBoost model on the performance, the pathfinder algorithm is used to optimise the model hyperparameter settings and improve the fault diagnosis accuracy. Experimental results demonstrate the performance of the proposed improved LDAE‐VMD‐XGBoost in accurate nuclear power turbine vibration fault diagnosis. |
| format | Article |
| id | doaj-art-2ed4f7c15d134aea8a2178facc80d1ff |
| institution | OA Journals |
| issn | 2516-8398 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Collaborative Intelligent Manufacturing |
| spelling | doaj-art-2ed4f7c15d134aea8a2178facc80d1ff2025-08-20T01:55:12ZengWileyIET Collaborative Intelligent Manufacturing2516-83982024-09-0163n/an/a10.1049/cim2.12108Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithmRuirui Zhong0Yixiong Feng1Puyan Li2Xuanyu Wu3Ao Guo4Ansi Zhang5Chuanjiang Li6State Key Laboratory of Public Big Data Guizhou University Guiyang ChinaState Key Laboratory of Public Big Data Guizhou University Guiyang ChinaState Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou ChinaState Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou ChinaState Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou ChinaState Key Laboratory of Public Big Data Guizhou University Guiyang ChinaState Key Laboratory of Public Big Data Guizhou University Guiyang ChinaAbstract Nuclear power turbine fault diagnosis is an important issue in the field of nuclear power safety. The numerous state parameters in the operation and maintenance of nuclear power turbines are collected, forming a complex high‐dimensional feature space. These high‐dimensional feature spaces contain redundant information, which increases the training cost and reduces the recognition accuracy and efficiency of the fault diagnosis model. To address the aforementioned challenges, a vibration fault diagnosis algorithm in nuclear power turbines is proposed. First, a long short‐term memory‐based denoising autoencoder (LDAE) is designed to enhance the capability of uncertainty awareness. Then, a feature extraction method integrating variational mode decomposition (VMD), L‐cliffs‐based effective mode selection, and sample entropy is devised to extract the latent features from the complex high‐dimensional feature space. Furthermore, using extreme gradient boosting (XGBoost) as the classifier, LDAE‐VMD‐XGBoost model is constructed for fault diagnosis of nuclear power turbines. Considering the impact of multiple hyperparameters of LDAE‐VMD‐XGBoost model on the performance, the pathfinder algorithm is used to optimise the model hyperparameter settings and improve the fault diagnosis accuracy. Experimental results demonstrate the performance of the proposed improved LDAE‐VMD‐XGBoost in accurate nuclear power turbine vibration fault diagnosis.https://doi.org/10.1049/cim2.12108fault diagnosisfeature extractiongenetic algorithmsintelligent manufacturing systems |
| spellingShingle | Ruirui Zhong Yixiong Feng Puyan Li Xuanyu Wu Ao Guo Ansi Zhang Chuanjiang Li Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm IET Collaborative Intelligent Manufacturing fault diagnosis feature extraction genetic algorithms intelligent manufacturing systems |
| title | Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm |
| title_full | Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm |
| title_fullStr | Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm |
| title_full_unstemmed | Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm |
| title_short | Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm |
| title_sort | uncertainty aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm |
| topic | fault diagnosis feature extraction genetic algorithms intelligent manufacturing systems |
| url | https://doi.org/10.1049/cim2.12108 |
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