计算机与现代化Issue(12)：19-25,7.DOI:10.3969/j.issn.1006-2475.2025.12.003
基于卷积双通道多层感知机混合器和加权投票机制的风电机组齿轮箱故障诊断
Fault Diagnosis of Wind Turbine Gearbox Based on Convolutional Dual Channel Multilayer Perceptron Mixer and Weighted Voting Mechanism
摘要
Abstract
The gearbox is one of the key and vulnerable components in wind turbines,and the fault diagnosis of its health condi-tion is of great significance to reduce operation and maintenance costs and improve cost efficiency.Therefore,a fault diagnosis method of wind turbine gearbox based on convolutional double-channel multi-layer perceptron mixer and weighted voting mecha-nism is proposed.Firstly,the original vibration signal is transformed into two-dimensional time-frequency image by continuous wavelet transform.Then,the local features of 2D time-frequency images are extracted by using a two-dimensional convolutional network,and the time-frequency image data is divided into non-overlapping patches.A two-channel multi-layer perceptron mixer network is constructed to extract the global features.Finally,the extracted two global feature vectors are weighted to get the final feature representation,and the final fault diagnosis result is obtained through the full connection layer classification.The test results of UConn gearbox dataset show that the proposed method has higher diagnostic performance than other traditional methods,and achieves the highest diagnostic accuracy of 100%.关键词
风电机组齿轮箱/故障诊断/卷积网络/双通道多层感知机混合器/加权投票机制Key words
wind turbine gearbox/fault diagnosis/convolutional network/dual channel multi-layer perceptron mixer/weighted voting mechanism分类
信息技术与安全科学引用本文复制引用
王望龙,徐军杨,苏鹏,付文龙..基于卷积双通道多层感知机混合器和加权投票机制的风电机组齿轮箱故障诊断[J].计算机与现代化,2025,(12):19-25,7.基金项目
国家自然科学基金资助项目(51741907) （51741907）
