基于边缘智能的电磁能装备轻量化故障诊断方法  

The Lightweight Fault Diagnosis Method of Electromagnetic Energy Equipment Based on Edge Intelligence

作　　者：单南良 徐兴华[1] 鲍先强 丁启翔 廖涛 Shan Nanliang;Xu Xinghua;Bao Xianqiang;Ding Qixiang;Liao Tao(National Key Laboratory of Electromagnetic Energy Naval University of Engineering,Wuhan 430033 China)

机构地区：[1]电磁能技术全国重点实验室(海军工程大学),武汉430033

出　　处：《电工技术学报》2025年第3期821-831,共11页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金(62102436);湖北省自然科学基金(2020CFB339,2021CFB279)资助项目。

摘　　要：随着海量状态监测数据的获取,复杂电磁能装备的关键部件健康状态监测对于实时性和可靠性的要求不断增加,研究利用边缘智能技术赋能装备故障诊断是一种很有发展前景的方法。边缘智能技术致力于将智能算法和算力资源下沉到设备端,在靠近数据源的位置对数据进行处理,能够很好地解决工业嵌入式系统资源受限和海量数据传输所带来的故障诊断时延,防止设备过度损坏。该文提出一种基于边缘智能的轻量化故障诊断方法,在数据采集过程中利用压缩感知技术将密集型的多元监测数据非线性压缩为稀疏采样数据,故障诊断模型集成了深度极限学习机和核函数,深度挖掘压缩采样信号与故障类型之间的内在联系。通过模型轻量化技术,将诊断模型部署在设备端的边缘智能计算卡上,显著降低了数据的传输、计算和存储压力,从而提高了智能故障诊断的实时性。With the proliferation of massive state monitoring data,the health status monitoring of critical components in complex electromagnetic energy equipment demands increasingly stringent requirements for realtime and reliable performance.Edge intelligence technology,dedicated to decentralizing intelligent algorithms and computational resources to the device end,presents a promising approach to addressing the challenges of fault diagnosis in such systems.This paper introduces a novel lightweight fault diagnosis method leveraging edge intelligence,which processes data near its source,mitigating issues related to limited resources in industrial embedded systems and the delays caused by massive data transmission.At the heart of our proposed method is the application of compressive sensing during data acquisition.This technique effectively compresses dense,multivariate monitoring data into a sparse form,significantly reducing data volume while preserving critical fault-related information.The subsequent integration of deep extreme learning machines(DELM)and kernel functions allows for a profound exploration of the relationships between the compressed data and potential faults.The model is then refined through lightweight techniques,making it suitable for deployment on edge computing devices,thus minimizing the demands on data transmission,computation,and storage resources.The effectiveness of our method is empirically validated through rigorous testing on an EdgeBoard AI computing platform,a device designed to emulate real-world industrial conditions.The results are compelling:the method achieves a diagnostic accuracy exceeding 99%,with a diagnosis time well within the millisecond range,underscoring its potential for real-time industrial applications.A detailed analysis of the impact of compression ratio on diagnostic efficiency reveals that an 80%compression rate offers the optimal balance between accuracy and speed.Moreover,the incorporation of particle swarm optimization(PSO)into the model further enhances its performa

关 键 词：压缩感知 深度极限学习机 核函数 轻量化故障诊断 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] TM15[自动化与计算机技术—计算机科学与技术]