面向大规模工业生产过程的数据驱动故障诊断方法综述  

A survey of data-driven fault-diagnosis methods for large-scale industrial production processes

作　　者：于骞翔 李擎[1,2] 李琳琳 王义轩[1] YU Qianxiang;LI Qing;LI Linlin;WANG Yixuan(School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China;Key Laboratory of Knowledge Automation for Industrial Processes(Ministry of Education),Beijing 100083,China)

机构地区：[1]北京科技大学自动化学院,北京100083 [2]工业过程知识自动化教育部重点实验室,北京100083

出　　处：《工程科学学报》2025年第4期780-793,共14页Chinese Journal of Engineering

基　　金：国家优秀青年科学基金资助项目(62322303);国家自然科学基金资助面上项目(62073029,62273033)。

摘　　要：聚焦于大规模工业生产过程智能化、精准化和多源化的需求,故障诊断对保障工业生产过程的安全可靠运行与实时有效维护具有重要意义.数据驱动方法作为一种创新范式,通过融合历史数据、实时数据以及多源信息,避免了对精确模型的依赖,能够有效提升故障检测与识别的准确率和效率.首先,本文梳理了数据驱动框架下的故障诊断方法,着重探讨了信号处理、统计模式识别、多元统计等系统稳态特性分析方法,并针对系统的动态、非线性和非高斯分布等复杂特性,进一步综述了动态多元统计、子空间辨识、深度学习和核空间投影等故障诊断方法.其次,介绍了大规模工业生产过程的分布式故障诊断方法.从系统的分布式结构和分布式传感器网络出发,分别阐述了该方法在系统分解和数据融合、相关性分析以及一致性方法等三个方面的最新进展.分布式故障诊断方法将监测职能分散到各子单元,使各子单元可根据自身及相邻子单元的运行状态自行做出安全性能判断,在大规模工业生产过程的监测和故障诊断中具有优势.最后,总结了数据驱动的分布式故障诊断方法的实际应用,并指出其在定性定量混合分析、鲁棒性诊断和数据安全等方面的发展趋势.Fault diagnosis for large-scale industrial production systems has attracted considerable research interest in response to the complex,multisource,and precision requirements of these processes.Fault diagnosis is crucial for the safe,reliable,and real-time maintenance of industrial production processes.This work presents a comprehensive survey of fault-diagnosis methods and emphasizes two cornerstone strategies:data-driven paradigms and distributed methods.Traditional fault-diagnosis methods based on mechanism have limited applications because precise modeling of the systems considered is required.Data-driven approaches avoid the dependence on precise modeling;thus,the research focus on fault diagnosis for industrial production processes has gradually shifted from mechanism-based to data-driven methods that integrate historical data,real-time data,and multisource information to enhance the accuracy and efficiency of the fault detection and identification approaches.A comprehensive overview of data-driven fault-diagnosis methods is given in the first part of this work.Specifically,smooth data from industrial processes is collected and used for fault diagnosis.The internal state variables may continuously change,and accompanied with time correlation among the process measurements.Integrating data-driven and dynamic analyses,necessitated by the dynamic nature of the process variables,offers a more accurate representation of system behavior.This can be achieved through the introduction of time-series modeling in basic multivariate statistics and the capture of dynamic properties by subspace identification.Furthermore,deep learning and kernel methods address nonlinearity,and non-Gaussian traits are tackled by independent component analysis(ICA)and other methods.Second,distributed fault-diagnosis methods for large-scale industrial production processes are reviewed.The usual fault-diagnosis methods for large-scale systems rely on centralized sensor network monitoring.Centralization necessitates consolidated data processin

关 键 词：大规模系统 故障诊断 数据驱动 动态特性 分布式 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]