电力系统保护与控制2026，Vol.54Issue(1)：83-93,11.DOI:10.19783/j.cnki.pspc.250397
基于双分支-交叉注意力融合的风电齿轮箱故障诊断方法
Wind turbine gearbox fault diagnosis method based on dual-branch cross-attention fusion
摘要
Abstract
Aiming at the problems that fault diagnosis data of wind turbine gearboxes have time series and the single-channel model is difficult to effectively extract the composite fault feature information,a fault diagnosis method integrating an improved orthogonal convolutional capsule network(OCCN)and a bidirectional long short-term memory neural network(BiLSTM)is proposed.First,the original signals are preprocessed.Then,the preprocessed signals are fed into a constructed OCCN-BiLSTM dual-branch model to extract the spatial features and time domain features of composite faults,respectively.Finally,the extracted spatiotemporal features are fused through a cross-attention mechanism and input into a fully connected layer for signal classification,enabling intelligent fault diagnosis of wind turbine gearboxes.Experimental results show that the proposed diagnosis method can effectively achieve intelligent fault diagnosis for wind turbine gearboxes,with an accuracy of 99.53%on the test set.关键词
故障诊断/胶囊网络/并行双通道/特征融合/风电齿轮箱Key words
fault diagnosis/capsule network/parallel dual-channel architecture/feature fusion/wind turbine gearbox引用本文复制引用
孙抗,李腾飞,王浩,杨明,赵来军..基于双分支-交叉注意力融合的风电齿轮箱故障诊断方法[J].电力系统保护与控制,2026,54(1):83-93,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.U1804143). 国家自然科学基金项目资助(U1804143) （No.U1804143）
河南省科技攻关计划项目资助(242102241056) （242102241056）
