Zero-shot intelligent fault diagnosis via semantic fusion embedding
Most fault diagnosis studies rely on the man-made data collected in laboratory where the operation conditions are under control and stable. However, they can hardly adapt to the practical conditions since the man-made data can hardly model the fault patterns across domains. Aiming to solve this prob...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-01-01
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| Series: | Cognitive Robotics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667241324000284 |
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| _version_ | 1841556943935635456 |
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| author | Honghua Xu Zijian Hu Ziqiang Xu Qilong Qian |
| author_facet | Honghua Xu Zijian Hu Ziqiang Xu Qilong Qian |
| author_sort | Honghua Xu |
| collection | DOAJ |
| description | Most fault diagnosis studies rely on the man-made data collected in laboratory where the operation conditions are under control and stable. However, they can hardly adapt to the practical conditions since the man-made data can hardly model the fault patterns across domains. Aiming to solve this problem, this paper proposes a novel deep fault semantic fusion embedding model (DFSFEM) to realize zero-shot intelligent fault diagnosis. The novelties of DFSFEM lie in two aspects. On the one hand, a novel semantic fusion embedding module is proposed to enhance the representability and adaptability of the feature learning across domains. On the other hand, a neural network-based metric module is designed to replace traditional distance measurements, enhancing the transferring capability between domains. These novelties jointly help DFSFEM provide prominent faithful diagnosis on unseen fault types. Experiments on bearing datasets are conducted to evaluate the zero-shot intelligent fault diagnosis performance. Extensive experimental results and comprehensive analysis demonstrate the superiority of the proposed DFSFEM in terms of diagnosis correctness and adaptability. |
| format | Article |
| id | doaj-art-6968a2cacb43499eb7de263760187908 |
| institution | Kabale University |
| issn | 2667-2413 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Cognitive Robotics |
| spelling | doaj-art-6968a2cacb43499eb7de2637601879082025-01-07T04:17:37ZengKeAi Communications Co. Ltd.Cognitive Robotics2667-24132025-01-0153747Zero-shot intelligent fault diagnosis via semantic fusion embeddingHonghua Xu0Zijian Hu1Ziqiang Xu2Qilong Qian3Corresponding author.; State Grid Nanjing Power Supply Company, Nanjing, 210000, PR ChinaState Grid Nanjing Power Supply Company, Nanjing, 210000, PR ChinaState Grid Nanjing Power Supply Company, Nanjing, 210000, PR ChinaState Grid Nanjing Power Supply Company, Nanjing, 210000, PR ChinaMost fault diagnosis studies rely on the man-made data collected in laboratory where the operation conditions are under control and stable. However, they can hardly adapt to the practical conditions since the man-made data can hardly model the fault patterns across domains. Aiming to solve this problem, this paper proposes a novel deep fault semantic fusion embedding model (DFSFEM) to realize zero-shot intelligent fault diagnosis. The novelties of DFSFEM lie in two aspects. On the one hand, a novel semantic fusion embedding module is proposed to enhance the representability and adaptability of the feature learning across domains. On the other hand, a neural network-based metric module is designed to replace traditional distance measurements, enhancing the transferring capability between domains. These novelties jointly help DFSFEM provide prominent faithful diagnosis on unseen fault types. Experiments on bearing datasets are conducted to evaluate the zero-shot intelligent fault diagnosis performance. Extensive experimental results and comprehensive analysis demonstrate the superiority of the proposed DFSFEM in terms of diagnosis correctness and adaptability.http://www.sciencedirect.com/science/article/pii/S2667241324000284Fault diagnosisZero-shot learningSemantic fusion embeddingDomain transferring |
| spellingShingle | Honghua Xu Zijian Hu Ziqiang Xu Qilong Qian Zero-shot intelligent fault diagnosis via semantic fusion embedding Cognitive Robotics Fault diagnosis Zero-shot learning Semantic fusion embedding Domain transferring |
| title | Zero-shot intelligent fault diagnosis via semantic fusion embedding |
| title_full | Zero-shot intelligent fault diagnosis via semantic fusion embedding |
| title_fullStr | Zero-shot intelligent fault diagnosis via semantic fusion embedding |
| title_full_unstemmed | Zero-shot intelligent fault diagnosis via semantic fusion embedding |
| title_short | Zero-shot intelligent fault diagnosis via semantic fusion embedding |
| title_sort | zero shot intelligent fault diagnosis via semantic fusion embedding |
| topic | Fault diagnosis Zero-shot learning Semantic fusion embedding Domain transferring |
| url | http://www.sciencedirect.com/science/article/pii/S2667241324000284 |
| work_keys_str_mv | AT honghuaxu zeroshotintelligentfaultdiagnosisviasemanticfusionembedding AT zijianhu zeroshotintelligentfaultdiagnosisviasemanticfusionembedding AT ziqiangxu zeroshotintelligentfaultdiagnosisviasemanticfusionembedding AT qilongqian zeroshotintelligentfaultdiagnosisviasemanticfusionembedding |