Wind Turbine Gearbox Fault Diagnosis Based on Multi-sensor Signals Fusion  

作　　者：Yao Zhao Ziyu Song Dongdong Li Rongrong Qian Shunfu Lin 

机构地区：[1]the College of Electrical Engineering,Shanghai University of Electric Power,Shanghai 200090,China [2]IEEE [3]the Design Research and Development Center AECC Commercial Aircraft Engine Co.,Ltd.,Shanghai 200241,China

出　　处：《Protection and Control of Modern Power Systems》2024年第4期96-109,共14页现代电力系统保护与控制（英文）

基　　金：supported by the Shanghai Rising-Star Program(No.21QC1400200);the Natural Science Foundation of Shanghai(No.21ZR1425400);the National Natural Science Foundation of China(No.52377111).

摘　　要：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.

关 键 词：Wind turbine gearbox fault diagnosis multiple scenarios deep learning stacked denoising au-to-encoder cycle reservoir with regular jumps feature fusion network 

分 类 号：TM614[电气工程—电力系统及自动化] TP212[自动化与计算机技术—检测技术与自动化装置]