A Lightweight Fault Diagnosis Framework for Hydro-Turbine Main Shaft Bearing Under Noise Interference
As a critical component of hydro-generating units, the main shaft bearing of a hydro-turbine is essential for ensuring the safety and stability of the unit. However, in industrial environments, operational noise often interferes with fault diagnosis accuracy for main shaft bearings. Thus, this paper...
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| Format: | Article |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11030619/ |
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| author | Hongwei Zhang Zhao Liu Hansong Si Kaipeng Yu Shuaifang Li Zhenwu Yan |
| author_facet | Hongwei Zhang Zhao Liu Hansong Si Kaipeng Yu Shuaifang Li Zhenwu Yan |
| author_sort | Hongwei Zhang |
| collection | DOAJ |
| description | As a critical component of hydro-generating units, the main shaft bearing of a hydro-turbine is essential for ensuring the safety and stability of the unit. However, in industrial environments, operational noise often interferes with fault diagnosis accuracy for main shaft bearings. Thus, this paper proposes a novel noise-robust hydro-turbine fault diagnosis framework. This framework integrates and enhances the local feature extraction capabilities of convolutional neural networks with the global feature extraction capabilities of Transformers. First, Noise-adaptive Random Convolution is employed to randomly perturb the signal, enabling the extraction of noise-robust sample features while preserving the periodic characteristics. Moreover, it introduces only a minimal number of parameters. Second, a parameter-free Light Global Attention mechanism is proposed, which distinguishes key features from noise interference by minimizing the energy differences among similar features. Comparative experiments demonstrate that the lightweight method proposed in this paper exhibits superior diagnostic performance and noise robustness. |
| format | Article |
| id | doaj-art-c570f6ccbbc547f98c7a2ceed60b0ee6 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c570f6ccbbc547f98c7a2ceed60b0ee62025-08-20T02:40:13ZengIEEEIEEE Access2169-35362025-01-011310213310214310.1109/ACCESS.2025.357868211030619A Lightweight Fault Diagnosis Framework for Hydro-Turbine Main Shaft Bearing Under Noise InterferenceHongwei Zhang0https://orcid.org/0009-0009-0982-1693Zhao Liu1Hansong Si2Kaipeng Yu3Shuaifang Li4Zhenwu Yan5China Yangtze Power Company Ltd., Yibin, Sichun, ChinaChina Yangtze Power Company Ltd., Yibin, Sichun, ChinaChina Yangtze Power Company Ltd., Yibin, Sichun, ChinaChina Yangtze Power Company Ltd., Yibin, Sichun, ChinaChina Yangtze Power Company Ltd., Yibin, Sichun, ChinaChina Yangtze Power Company Ltd., Yibin, Sichun, ChinaAs a critical component of hydro-generating units, the main shaft bearing of a hydro-turbine is essential for ensuring the safety and stability of the unit. However, in industrial environments, operational noise often interferes with fault diagnosis accuracy for main shaft bearings. Thus, this paper proposes a novel noise-robust hydro-turbine fault diagnosis framework. This framework integrates and enhances the local feature extraction capabilities of convolutional neural networks with the global feature extraction capabilities of Transformers. First, Noise-adaptive Random Convolution is employed to randomly perturb the signal, enabling the extraction of noise-robust sample features while preserving the periodic characteristics. Moreover, it introduces only a minimal number of parameters. Second, a parameter-free Light Global Attention mechanism is proposed, which distinguishes key features from noise interference by minimizing the energy differences among similar features. Comparative experiments demonstrate that the lightweight method proposed in this paper exhibits superior diagnostic performance and noise robustness.https://ieeexplore.ieee.org/document/11030619/Attention mechanismconvolutional neural networkdeep learningfault diagnosishydro-turbinehydro-generating unit |
| spellingShingle | Hongwei Zhang Zhao Liu Hansong Si Kaipeng Yu Shuaifang Li Zhenwu Yan A Lightweight Fault Diagnosis Framework for Hydro-Turbine Main Shaft Bearing Under Noise Interference IEEE Access Attention mechanism convolutional neural network deep learning fault diagnosis hydro-turbine hydro-generating unit |
| title | A Lightweight Fault Diagnosis Framework for Hydro-Turbine Main Shaft Bearing Under Noise Interference |
| title_full | A Lightweight Fault Diagnosis Framework for Hydro-Turbine Main Shaft Bearing Under Noise Interference |
| title_fullStr | A Lightweight Fault Diagnosis Framework for Hydro-Turbine Main Shaft Bearing Under Noise Interference |
| title_full_unstemmed | A Lightweight Fault Diagnosis Framework for Hydro-Turbine Main Shaft Bearing Under Noise Interference |
| title_short | A Lightweight Fault Diagnosis Framework for Hydro-Turbine Main Shaft Bearing Under Noise Interference |
| title_sort | lightweight fault diagnosis framework for hydro turbine main shaft bearing under noise interference |
| topic | Attention mechanism convolutional neural network deep learning fault diagnosis hydro-turbine hydro-generating unit |
| url | https://ieeexplore.ieee.org/document/11030619/ |
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