Gas turbine rotor fault diagnosis method based on WDCNNSVM deep transfer learning

Home > Archive>Volume 35, Issue 11, 2021 >115-123

Gas turbine rotor fault diagnosis method based on WDCNNSVM deep transfer learning
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                        Tang JingpengTang Jingpeng
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,Beijing 100192, China; 2.Intelligent Perception and Control of Highend Equipment Beijing International Science and Technology Cooperation Base, Beijing 100192, China
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Wang HongjunWang Hongjun
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,Beijing 100192, China; 2.Intelligent Perception and Control of Highend Equipment Beijing International Science and Technology Cooperation Base, Beijing 100192, China
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Zhong JianlinZhong Jianlin
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,Beijing 100192, China; 2.Intelligent Perception and Control of Highend Equipment Beijing International Science and Technology Cooperation Base, Beijing 100192, China
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Liu ShucongLiu Shucong
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,Beijing 100192, China; 2.Intelligent Perception and Control of Highend Equipment Beijing International Science and Technology Cooperation Base, Beijing 100192, China
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Zhang XiangZhang Xiang
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,Beijing 100192, China; 2.Intelligent Perception and Control of Highend Equipment Beijing International Science and Technology Cooperation Base, Beijing 100192, China
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Xu WufengXu Wufeng
College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
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Abstract:

In industries for gas turbine rotor, there are a large number of normal operation vibration signal sample data and few fault data,which caused fault diagnosis accuracy lower. A gas turbine rotor deep transfer learning fault diagnosis method is proposed. First, a firstlayer wide convolutional kernel deep convolutional neural network (WDCNN) model is pretrained with a typical industry sample dataset, obtained the model initial weights. Second, in the source domain, the weights of the WDCNN model are updated using a large number of normal operation samples obtained from the test drive of a certain type of gas turbine; In the target domain, the normal and fault data sample characteristics of the gas turbine are extracted by using the convolutional layer trained in the source domain, and then the support vector machines (SVM) are used for classification identification, so as to achieve the gas turbine fault identification. The experimental results of the test data show that the method identification accuracy is 96%, which verifies the feasibility of migrating the pretrained deep learning model of the bearing dataset to the field of gas turbine rotor for fault diagnosis.

Key words:gas turbine rotor system; vibration analysis; transfer learning; fault diagnosis

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Online: June 08,2022
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