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| Wind Turbine Gearbox Fault Diagnosis Based on Multi-Sensor Signals Fusion |
| Yao Zhao, Member, IEEE,Ziyu Song,Dongdong Li, Member, IEEE,Rongrong Qian,Shunfu Lin, Member, IEEE |
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| Abstract: |
| This paper proposes a novel fault diagnosis method by fusing the information from multi-sensor signals to improve the reliability of the conventional vibration-based wind turbine drivetrain gearbox fault diagnosis methods. The method fully extracts fault features for variable speed, insufficient samples, and strong noise scenarios that may occur in the actual operation of a wind turbine planetary gearbox. First, multiple sensor signals are added to the diagnostic model, and multiple stacked denoising auto-encoders are designed and improved to extract the fault information. Then, a cycle reservoir with regular jumps is introduced to fuse multidimensional fault information and output diagnostic results in response to the insufficient ability to process fused information by the conventional Softmax classifier. In addition, the competitive swarm optimizer algorithm is introduced to address the challenge of obtaining the optimal combination of parameters in the network. Finally, the validation results show that the proposed method can increase fault diagnostic accuracy and improve robustness. |
| Key words: Wind turbine gearbox, fault diagnosis, multiple scenarios, deep learning, stacked denoising auto-encoder, cycle reservoir with regular jumps, feature fusion network. |
| DOI：10.23919/PCMP.2023.000241 |
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| Fund:This work is supported by the Shanghai Rising-Star Program (No. 21QC1400200), the Natural Science Foundation of Shanghai (No. 21ZR1425400), and the National Natural Science Foundation of China (No. 52377111). |
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