基于边缘智能的电磁能装备轻量化故障诊断方法

doi:10.19595/j.cnki.1000-6753.tces.240230

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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,能够很好的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。本文提出了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。最后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进行部署,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。

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	单南良
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关键词 ：压缩感知, 深度极限学习机, 核函数, 轻量化故障诊断

Abstract：

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 real-time 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 performance by dynamically optimizing key parameters, leading to superior generalization and classification accuracy.
The robustness of our method is further demonstrated through its application on a fault data simulation platform, where it is compared against state-of-the-art fault diagnosis techniques. The results indicate that our method not only requires less input data dimensionality but also significantly outperforms other methods in terms of diagnostic time and accuracy. The use of temporal-spatial features, extracted through a combination of convolutional neural networks (CNN) for spatial learning and simple recurrent units (SRU) for temporal dynamics, contributes to the model's high performance. The SRU, in particular, stands out for its ability to mitigate the gradient disappearance problem and reduce computational costs compared to traditional recurrent neural networks like LSTM and GRU. The model's resilience to input data noise, as evidenced by the inclusion of Gaussian noise in the tests, further attests to its robustness in practical scenarios.
The study concludes that the edge intelligence-based fault diagnosis method is not only a significant advancement over existing methods in terms of real-time processing and diagnostic precision but also substantially alleviates the computational and transmission burdens associated with fault diagnosis. The successful validation on both the EdgeBoard AI platform and the fault data simulation platform confirms the method's practical applicability and its potential to revolutionize the field of intelligent fault diagnosis for electromagnetic energy equipment.

Key words： Compressed sensing deep extreme learning machine kernel function lightweight fault diagnosis

收稿日期: 2024-02-02

PACS:

TP39

基金资助:

国家自然科学基金（62102436）和湖北省自然科学基金（2020CFB339,2021CFB279）资助

通讯作者: 廖涛男,1987年生,助理研究员,研究方向为电磁发射技术,故障诊断预测等。E-mail：xgliaotao@163.com

作者简介: 单南良男,1995年生,博士研究生,研究方向人工智能技术在复杂装备系统中的应用。E-mail：21001401@nue.edu.cn

引用本文:

单南良, 徐兴华, 鲍先强, 丁启翔, 廖涛. 基于边缘智能的电磁能装备轻量化故障诊断方法[J]. 电工技术学报, 0, (): 2492914-2492914. Shan Nanliang, Xu Xinghua, Bao Xianqiang, Ding Qixiang, Liao Tao. The Lightweight Fault Diagnosis Method of Electromagnetic Energy Equipment Based on Edge Intelligence. Transactions of China Electrotechnical Society, 0, (): 2492914-2492914.

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