基于 MK-KNN 图和 ESSGAT 的轴承故障诊断模拟数据驱动半监督框架。

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yuyan Li , Tiantian Wang , Jingsong Xie , Jinsong Yang , Tongyang Pan , Buyao Yang
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引用次数: 0

摘要

目前的有监督智能故障诊断依赖于丰富的标记数据。然而,收集和标记数据通常既昂贵又耗时。使用无标记数据进行故障诊断仍然是一项重大挑战。为解决这一问题,我们提出了一种基于多核 K 近邻(MK-KNN)和边缘自监督图注意网络(ESSGAT)的仿真数据驱动半监督框架。新颖的 MK-KNN 建立了模拟数据与真实数据之间的邻域关系。开发的多核函数可降低过拟合和欠拟合的风险,从而增强模拟-真实图的鲁棒性。所设计的 ESSGAT 采用两种形式的自监督关注来预测边缘的存在,增加 MK-KNN 图中关键相邻节点的权重。利用公共轴承数据集和高速列车轴箱轴承自建数据集评估了所提方法的性能。结果表明,与其他最先进的图构建方法和图卷积网络相比,所提出的方法实现了更好的诊断性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A simulation data-driven semi-supervised framework based on MK-KNN graph and ESSGAT for bearing fault diagnosis
Current supervised intelligent fault diagnosis relies on abundant labeled data. However, collecting and labeling data are typically both expensive and time-consuming. Fault diagnosis with unlabeled data remains a significant challenge. To address this issue, a simulation data-driven semi-supervised framework based on multi-kernel K-nearest neighbor (MK-KNN) and edge self-supervised graph attention network (ESSGAT) is proposed. The novel MK-KNN establishes the neighborhood relationships between simulation data and real data. The developed multi-kernel function mitigates the risks of overfitting and underfitting, thereby enhancing the robustness of the simulation-real graphs. The designed ESSGAT employs two forms of self-supervised attention to predict the presence of edges, increasing the weights of crucial neighboring nodes in the MK-KNN graph. The performance of the proposed method is evaluated using a public bearing dataset and a self-constructed dataset of high-speed train axle box bearings. The results show that the proposed method achieves better diagnostic performance compared with other state-of-the-art graph construction methods and graph convolutional networks.
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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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