Diagnosis of steam turbine rotor based on improved convolutional neural network algorithm
Abstract To address the challenges of insufficient precision and limited adaptability in conventional rotor fault diagnosis methods, we propose a new approach using convolutional neural networks. This aims to effectively identify complex and diverse fault patterns in a steam turbine rotor through a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-025-00269-x |
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| _version_ | 1850042517326659584 |
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| author | Zhongtao Zhou Miao Zhou Hui Huang Yanghai Li Wanbing Xu |
| author_facet | Zhongtao Zhou Miao Zhou Hui Huang Yanghai Li Wanbing Xu |
| author_sort | Zhongtao Zhou |
| collection | DOAJ |
| description | Abstract To address the challenges of insufficient precision and limited adaptability in conventional rotor fault diagnosis methods, we propose a new approach using convolutional neural networks. This aims to effectively identify complex and diverse fault patterns in a steam turbine rotor through a thorough examination and in-depth investigation. An HZXT-009 sliding ball bearing simulation rig was used to conduct fault tests such as rotor misalignment, unbalance, and touching faults. The data collected experimentally was used to perform a comprehensive analysis of the temporal and spectral signal characteristics. Use the obtained data to train and enhance the convolutional neural network fault diagnosis model. The results of the model testing accuracy can reach 99%. The generalization test is introduced to verify that the model trained by the simulation test data can detect multi-condition faults in the operation of the power plant. The network detection results show that the accuracy rate can reach 97.5%, which is expected to be widely used in actual production and improve the efficiency and accuracy of fault diagnosis of rotor system. |
| format | Article |
| id | doaj-art-ddf5328024e94843b934f47ca7689143 |
| institution | DOAJ |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-ddf5328024e94843b934f47ca76891432025-08-20T02:55:31ZengSpringerDiscover Artificial Intelligence2731-08092025-04-015111410.1007/s44163-025-00269-xDiagnosis of steam turbine rotor based on improved convolutional neural network algorithmZhongtao Zhou0Miao Zhou1Hui Huang2Yanghai Li3Wanbing Xu4State Grid Hubei Electric Power Co., Ltd., Power Science Research InstituteState Grid Hubei Electric Power Co., Ltd., Power Science Research InstituteState Grid Hubei Electric Power Co., Ltd., Power Science Research InstituteState Grid Hubei Electric Power Co., Ltd., Power Science Research InstituteState Grid Hubei Electric Power Co., Ltd., Power Science Research InstituteAbstract To address the challenges of insufficient precision and limited adaptability in conventional rotor fault diagnosis methods, we propose a new approach using convolutional neural networks. This aims to effectively identify complex and diverse fault patterns in a steam turbine rotor through a thorough examination and in-depth investigation. An HZXT-009 sliding ball bearing simulation rig was used to conduct fault tests such as rotor misalignment, unbalance, and touching faults. The data collected experimentally was used to perform a comprehensive analysis of the temporal and spectral signal characteristics. Use the obtained data to train and enhance the convolutional neural network fault diagnosis model. The results of the model testing accuracy can reach 99%. The generalization test is introduced to verify that the model trained by the simulation test data can detect multi-condition faults in the operation of the power plant. The network detection results show that the accuracy rate can reach 97.5%, which is expected to be widely used in actual production and improve the efficiency and accuracy of fault diagnosis of rotor system.https://doi.org/10.1007/s44163-025-00269-xRotor systemsFault diagnosisCNNDeep learning |
| spellingShingle | Zhongtao Zhou Miao Zhou Hui Huang Yanghai Li Wanbing Xu Diagnosis of steam turbine rotor based on improved convolutional neural network algorithm Discover Artificial Intelligence Rotor systems Fault diagnosis CNN Deep learning |
| title | Diagnosis of steam turbine rotor based on improved convolutional neural network algorithm |
| title_full | Diagnosis of steam turbine rotor based on improved convolutional neural network algorithm |
| title_fullStr | Diagnosis of steam turbine rotor based on improved convolutional neural network algorithm |
| title_full_unstemmed | Diagnosis of steam turbine rotor based on improved convolutional neural network algorithm |
| title_short | Diagnosis of steam turbine rotor based on improved convolutional neural network algorithm |
| title_sort | diagnosis of steam turbine rotor based on improved convolutional neural network algorithm |
| topic | Rotor systems Fault diagnosis CNN Deep learning |
| url | https://doi.org/10.1007/s44163-025-00269-x |
| work_keys_str_mv | AT zhongtaozhou diagnosisofsteamturbinerotorbasedonimprovedconvolutionalneuralnetworkalgorithm AT miaozhou diagnosisofsteamturbinerotorbasedonimprovedconvolutionalneuralnetworkalgorithm AT huihuang diagnosisofsteamturbinerotorbasedonimprovedconvolutionalneuralnetworkalgorithm AT yanghaili diagnosisofsteamturbinerotorbasedonimprovedconvolutionalneuralnetworkalgorithm AT wanbingxu diagnosisofsteamturbinerotorbasedonimprovedconvolutionalneuralnetworkalgorithm |