深度学习在设备故障预测与健康管理中的应用
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1重庆工商大学 检测控制集成系统工程实验室重庆400067; 2重庆工商大学人工智能学院重庆400067; 3重庆工商大学 国家智能制造服务国际科技合作基地重庆400067; 4葡萄牙阿尔加维大学CEOT中心法鲁葡萄牙

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TP206

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国家自然科学基金资助项目(51775112)


Application of deep learning in equipment prognostics and health management
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1.Chongqing Engineering Laboratory for Detection Control and Integrated System, Chongqing Technology and Business University, Chongqing 400067, China; 2.College of Artificial Intelligence, Chongqing Technology and Business University, Chongqing 400067, China; 3.National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University,Chongqing 400067, China; 4.CEOT, Universidad do Algarve, Faro, Portugal

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    摘要:

    在智能制造背景下,大数据驱动的设备故障预测与健康管理日益受到各界重视。深度学习能够在层次结构的特征提取过程中发现更多的隐藏知识,在领域自适应方面具有良好的数据适应性,近年来逐渐成为设备故障预测与健康管理的研究热点,并在设备故障诊断和预测中得到了广泛的应用。通过系统回顾近年来深度学习在设备故障预测与健康管理中应用,总结、分类和解释关于这一热点主题的主要文献,讨论了各种体系结构和相关理论。在此基础上,阐述了深度学习在设备故障诊断和预测方面所取得的主要成果、面临的挑战、以及未来的发展趋势,为设备故障预测与健康管理领域选择、设计或实现深度学习架构,提供明确的方向。

    Abstract:

    In intelligent manufacturing, the prognostics and health management of the equipment driven by big data has been paid much attention. In recent years, because it can capture more hidden knowledge in the process of feature extraction of hierarchical structure and has good data adaptability across a variety of domains, deep learning has become a hot topic in the field of equipment health management. It has been widely used in equipment fault diagnostics and prognostics. This paper systematically reviews emerging literatures on the application of deep learning in equipment health management. It summarizes, classifies and explains main publications on this trendy topic. Various architectures and related theories are also discussed. As a review, this paper expounds the achievements, challenges and future development trends of the deep learning in the field of equipment fault diagnostics and prognostics. It provides a clear direction for practitioners including the industry to select, design or implement deep learning architecture for the equipment health management.

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陈志强,陈旭东,José Valente de Olivira,李川.深度学习在设备故障预测与健康管理中的应用[J].仪器仪表学报,2019,40(9):206-226

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  • 在线发布日期: 2020-08-20
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