Deep transfer learning methods for typical supervised tasks in industrial monitoring: state-of-the-art, challenges, and perspectives

Q2 Engineering
铮 柴, 嘉业 汪, 春晖 赵, 进良 丁, 优贤 孙
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引用次数: 1

Abstract

Deep transfer learning-based industrial monitoring methods have received considerable research attention in recent years, especially in typical industrial monitoring tasks, including fault diagnosis and soft sensor developments. Such methods mine and transfer knowledge from similar source domains to model the data in the target domain. They provide a new perspective for cross-domain industrial monitoring problems caused by varying conditions in actual scenarios. This survey systematically sorts the deep transfer learning methods for typical supervised tasks in industrial monitoring and classifies them into model-based, instance-based, and feature-based approaches. Subsequently, it introduces the basic ideas and state-of-the-art approaches in fault diagnosis and soft sensor development of different categories. Finally, from the perspectives of complexly limited data, evaluation of transferability and negative transfer problems, and the dynamic characteristics of industrial processes, the survey highlights the current challenges in cross-domain industrial monitoring and points to future research areas in this field.
工业监测中典型监督任务的深度迁移学习方法:现状、挑战和前景
近年来,基于深度迁移学习的工业监测方法受到了相当大的研究关注,尤其是在典型的工业监测任务中,包括故障诊断和软传感器开发。这种方法从相似的源域挖掘和转移知识,以对目标域中的数据进行建模。它们为实际场景中由不同条件引起的跨领域工业监测问题提供了一个新的视角。这项调查系统地对工业监控中典型监督任务的深度迁移学习方法进行了分类,并将其分为基于模型、基于实例和基于特征的方法。随后,介绍了不同类别的故障诊断和软传感器开发的基本思想和最新方法。最后,从复杂有限的数据、可转移性和负转移问题的评估以及工业过程的动态特征的角度来看,该调查强调了当前跨领域工业监测的挑战,并指出了该领域未来的研究领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
中国科学:信息科学
中国科学:信息科学 Engineering-Engineering (miscellaneous)
CiteScore
2.50
自引率
0.00%
发文量
1961
期刊介绍:
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