电机与控制应用2025，Vol.52Issue(6)：585-595,11.DOI:10.12177/emca.2025.043
基于GAF与并行混合网络的隔离开关机械故障诊断方法
Mechanical Fault Diagnosis Method for Disconnectors Based on GAF and Parallel Hybrid Networks
摘要
Abstract
[Objective]To achieve high-precision identification of mechanical faults in disconnectors,a parallel hybrid network incorporating attention mechanisms is proposed,which combines temporal and image features for intelligent diagnosis.[Methods]To fully exploit the feature information of dual-channel data,a bidirectional long short-term memory network was employed in the temporal channel to extract time-domain features from vibration signals,capturing the dynamic temporal variations of the signal and effectively reflecting the time-varying characteristics of mechanical faults.In the image channel,vibration signals were converted into two-dimensional images using Gramian angular fields,where polar coordinate mapping was utilized to preserve the temporal dynamics.A convolutional neural network was then used to extract key image features.Furthermore,a self-attention mechanism was introduced in the temporal channel and a channel attention mechanism in the image channel,enabling the model to adaptively adjust the weight of each channel,thereby emphasizing critical information and effectively reducing feature redundancy.[Results]Fault simulation experiments were conducted on GW4-126 type disconnectors,and vibration signals under four operating conditions were collected.The proposed method was compared with five other deep learning models.Experimental results demonstrated that the proposed method achieves a fault recognition accuracy exceeding 97%,effectively identifying typical mechanical faults such as mechanism jamming,looseness,and phase asynchrony.[Conclusion]The proposed parallel hybrid model overcomes the limitations of single-channel approaches by integrating two distinct types of feature information.The introduction of attention mechanisms enables the model to dynamically adjust weights,highlight salient features,and enhance the accuracy of fault identification.This method provides a reliable theoretical foundation and technical reference for the condition monitoring of disconnectors,holds significant potential for future fault diagnosis and equipment maintenance,and offers new insights for the development of smart grid technologies.关键词
隔离开关/机械故障/故障诊断/振动信号/格拉姆角场/卷积神经网络/注意力机制Key words
disconnector/mechanical fault/fault diagnosis/vibration signal/Gramian angular field/convolutional neural network/attention mechanism分类
动力与电气工程引用本文复制引用
申张亮,许洪华,陈旖旎,尹来宾,董媛,马宏忠..基于GAF与并行混合网络的隔离开关机械故障诊断方法[J].电机与控制应用,2025,52(6):585-595,11.基金项目
国网江苏省电力有限公司重点科技项目资助(J2024047) Funded by the Key Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(J2024047) （J2024047）
