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基于深度学习的化工过程故障检测与诊断研究综述 被引量:4

A review of research on fault detection and diagnosis of chemical process based on deep learning
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摘要 化工过程的故障检测与诊断对于现代化工系统的可靠性和安全性具有重要意义。深度学习作为一项新兴的技术,引起了学术界和工业界的广泛关注。从方法的角度出发,将基于深度学习的化工过程故障检测与诊断技术分为:基于自动编码器的方法、基于深度置信网络的方法、基于卷积神经网络的方法和基于循环神经网络的方法,并分别对4种方法的最新研究进展进行了系统的归纳和总结。最后从工业应用角度总结了一些主要的挑战,并从“数据”、“模型”和“可视化”3个方面展望了未来的发展方向。 The fault detection and diagnosis of the chemical process is of great significance to the reliability and safety of modern industrial systems.As an emerging technology,deep learning has attracted intense attention from academia and industry in the last ten years,because of the automated feature learning,powerful feature representation capability and excellent classification performance in solving complex problems.From the perspective of methodology,this review divides the fault detection and diagnosis technology of chemical process based on deep learning into autoencoder-based method,deep belief network-based method,convolutional neural network-based method and recurrent neural network-based method.After a brief introduction to the several deep learning models,this paper reviewed and summarized the latest research progress using the four methods systematically.Finally,some major challenges are summarized from the perspective of industrial application,and the future development directions are prospected from the three aspects of“data”,“model”and“visualization”.
作者 鲍宇 程硕 王靖涛 BAO Yu;CHENG Shuo;WANG Jingtao(School of Chemical Engineering and Technology,Tianjin University,Tianjin 300050,China)
出处 《化学工业与工程》 CAS CSCD 北大核心 2022年第2期9-22,共14页 Chemical Industry and Engineering
基金 国家重点研发计划(2019YFC1905805) 国家自然科学基金(22078229,21576185)。
关键词 化工过程 故障检测与诊断 深度学习 chemical process fault detection and diagnosis deep learning
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