在不同条件和故障模式下预测剩余使用寿命的对比域不变广义方法

IF 9.4 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL
Xiaoqi Xiao , Jianguo Zhang , Dan Xu
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引用次数: 0

摘要

随着工业设备变得越来越复杂,需要在各种条件下运行,并经常表现出多种故障模式,基于原始环境数据建立的传统深度学习模型变得不再适用。此外,在实际工业场景中,由于收集大量监测数据的成本或可行性限制,如果缺乏多个源域和目标域数据,域自适应和经典域泛化方法的泛化能力就会受到严重影响。本文提出了一种单域对比域不变泛化(CDIG)方法,用于估算不同条件和故障模式下的剩余使用寿命。该方法首先定义同源信号作为基础数据。随后,它通过鼓励两个特征提取过程来学习域不变特征,以尽可能相似地提取同源信号的潜在特征。此外,它还利用多种基于条件的关注、集合和新颖的均衡损失函数来调节领域不变特征的生成。最终,RUL 预测器通过源领域数据、运行条件和时间信息进行训练,以促进其在不同领域的适用性。案例研究表明,CDIG 在未知条件下取得了令人满意的预测结果,凸显了所提方法作为有效预测工具的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Contrastive domain-invariant generalization for remaining useful life prediction under diverse conditions and fault modes
As industrial equipment becomes increasingly complex, necessitating operation under varied conditions and often exhibiting diverse failure modes, traditional deep learning models built on data from the original environment become inapplicable. Moreover, in actual industrial scenarios, the generalization capability of Domain Adaptation and classic Domain Generalization methods is severely impacted when there is a lack of multiple source domain and target domain data, due to the cost or feasibility constraints associated with collecting extensive monitoring data. In this paper, a single domain Contrastive Domain-Invariant Generalization (CDIG) method for estimating the remaining useful life under different conditions and fault modes is proposed. This method first defines homologous signals as the foundational data. Subsequently, it learns domain-invariant features by encouraging two feature extraction processes to extract latent features of homologous signals as similarly as possible. Additionally, multiple condition-based attention, pooling, and a novel equalization loss function are utilized to regulate the generation of domain-invariant features. Ultimately, the RUL predictor is trained by source domain data, operational conditions, and temporal information to facilitate its applicability across diverse domains. Case studies demonstrate that CDIG achieves satisfactory predictive results under unseen conditions, highlighting the potential of the proposed method as an effective predictive tool.
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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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