基于数字孪生和领域泛化的未知工作条件下的自适应故障诊断

IF 9.4 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL
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引用次数: 0

摘要

近年来,基于领域适应的智能故障诊断已被用于解决网络物理系统中的领域转移问题;然而,由于需要充分获取目标数据,这限制了其在未知工作条件下的适用性。为了克服这些限制,人们引入了领域泛化技术,以增强故障诊断模型在未知工作条件下的运行能力。然而,现有的方法都需要从不同的源领域获取大量标注的训练数据,这在现实世界的工程场景中会因资源限制而面临挑战。此外,由于缺乏随时间更新诊断模型的机制,因此需要探索能够根据新的未知工作条件进行自主重构的自适应通用诊断模型。在这种情况下,本文提出了一种自适应故障诊断系统,该系统结合了几种范例,即共享知识的监控-分析-计划-执行(MAPE-K)、领域泛化网络模型(DGNM)和数字孪生(DT)。MAPE-K 循环可在运行时适应动态工业环境,无需人工干预。为了解决标注训练数据稀缺的问题,数字双胞胎被用来生成补充数据,并不断调整参数,以反映新的未知工作条件的动态变化。DGNM 结合了对抗学习和基于领域的差异度量,以增强特征多样性和泛化能力。多域数据增强的引入增强了特征多样性,促进了多域之间的关联学习,最终提高了特征表征的泛化能力。我们在三个公开的旋转机械数据集上对所提出的故障诊断系统进行了评估,结果表明,与其他最先进的方法相比,该系统在跨工作运行和跨机器任务方面具有更高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-adaptive fault diagnosis for unseen working conditions based on digital twins and domain generalization
In recent years, intelligent fault diagnosis based on domain adaptation has been used to address domain shifts in cyber–physical systems; however, the need for acquiring target data sufficiently limits their applicability to unseen working conditions. To overcome such limitations, domain generalization techniques have been introduced to enhance the capacity of fault diagnostic models to operate under unseen working conditions. Nevertheless, existing approaches assume access to extensive labeled training data from various source domains, posing challenges in real-world engineering scenarios due to resource constraints. Moreover, the absence of a mechanism for updating diagnostic models over time calls for the exploration of self-adaptive generalized diagnosis models that are capable of autonomous reconfiguration in response to new unseen working conditions. In such a context, this paper proposes a self-adaptive fault diagnosis system that combines several paradigms, namely Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K), Domain Generalization Network Models (DGNMs), and Digital Twins (DT). The MAPE-K loop enables run-time adaptation to dynamic industrial environments without human intervention. To address the scarcity of labeled training data, digital twins are used to generate supplementary data and continuously tune parameters to reflect the dynamics of new unseen working conditions. DGNM incorporates adversarial learning and a domain-based discrepancy metric to enhance feature diversity and generalization. The introduction of multi-domain data augmentation enhances feature diversity and facilitates learning correlations among multiple domains, ultimately improving the generalization of feature representations. The proposed fault diagnosis system has been evaluated on three publicly available rotating machinery datasets to demonstrate its higher performance in cross-work operation and cross-machine tasks compared to other state-of-the-art methods.
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来源期刊
Reliability Engineering & System Safety
Reliability Engineering & System Safety 管理科学-工程:工业
CiteScore
15.20
自引率
39.50%
发文量
621
审稿时长
67 days
期刊介绍: Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.
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