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复杂系统维护策略最新研究进展: 从视情维护到预测性维护

陆宁云 陈闯 姜斌 邢尹

陆宁云, 陈闯, 姜斌, 邢尹. 复杂系统维护策略最新研究进展: 从视情维护到预测性维护. 自动化学报, 2021, 47(1): 1−17 doi: 10.16383/j.aas.c200227
引用本文: 陆宁云, 陈闯, 姜斌, 邢尹. 复杂系统维护策略最新研究进展: 从视情维护到预测性维护. 自动化学报, 2021, 47(1): 1−17 doi: 10.16383/j.aas.c200227
Lu Ning-Yun, Chen Chuang, Jiang Bin, Xing Yin. Latest progress on maintenance strategy of complex system: from condition-based maintenance to predictive maintenance. Acta Automatica Sinica, 2021, 47(1): 1−17 doi: 10.16383/j.aas.c200227
Citation: Lu Ning-Yun, Chen Chuang, Jiang Bin, Xing Yin. Latest progress on maintenance strategy of complex system: from condition-based maintenance to predictive maintenance. Acta Automatica Sinica, 2021, 47(1): 1−17 doi: 10.16383/j.aas.c200227

复杂系统维护策略最新研究进展: 从视情维护到预测性维护

doi: 10.16383/j.aas.c200227
基金项目: 国家自然科学基金 (61873122), 中央高校基本科研业务费 (NC2020002)资助
详细信息
    作者简介:

    陆宁云:南京航空航天大学自动化学院教授. 主要研究方向为过程监测,故障诊断,预测维护理论与应用. 本文通信作者. E-mail: luningyun@nuaa.edu.cn

    陈闯:南京航空航天大学自动化学院博士研究生. 主要研究方向为基于数据驱动的故障预测与健康管理. E-mail: chenchuang@nuaa.edu.cn

    姜斌:南京航空航天大学自动化学院教授. 主要研究方向为智能故障诊断与容错控制及其在飞机、卫星和高速列车上的应用. E-mail: binjiang@nuaa.edu.cn

    邢尹:河海大学地球科学与工程学院博士研究生. 主要研究方向为基于数据驱动的滑坡灾害预测. E-mail: xingyincc@163.com

Latest Progress on Maintenance Strategy of Complex System: From Condition-based Maintenance to Predictive Maintenance

Funds: Supported by National Natural Science Foundation of China (61873122), and Fundamental Research Funds for the Central Universities (NC2020002)
  • 摘要:

    对于复杂、可修复的工程系统, 设备维护是确保系统安全性、可靠性、可用性的重要手段之一. 系统维护策略已经历修复性维护、定时维护、视情维护等多种维护策略. 其中, 视情维护是目前最受关注的维护策略, 它通过收集和评估系统的实时状态信息进行维护决策, 具有全寿命周期内系统可靠性高、运营维护成本低等优点. 近年来, 随着物联网技术、信息技术和人工智能的快速发展, 一种更新颖的视情维护策略——预测性维护逐渐成为领域研究热点. 本文首先简要回顾了系统维护策略的发展历程; 然后, 重点介绍了视情维护的研究进展, 根据决策支持技术的不同, 将视情维护划分为基于随机退化模型的视情维护和基于数据驱动的预测性维护, 对每类技术的发展分支与研究现状进行了疏理、分析和总结; 最后, 探讨了当前复杂系统维护策略面临的挑战性问题和可能的未来研究方向.

  • 图  1  系统维护决策的全过程

    Fig.  1  Overall process of system maintenance decision-making

    图  2  退化过程及失效阈值失效示意图

    Fig.  2  Illustration of a degradation process with failure threshold

    图  3  基于随机退化模型的CBM策略的一般步骤

    Fig.  3  General steps of CBM strategy based on stochastic degradation model

    图  4  数据驱动PdM的一般步骤

    Fig.  4  General steps for data-driven PdM

    图  5  在线寿命预测与维护决策之间的关系

    Fig.  5  Relationship between online life prediction and maintenance decision-making

    图  6  传统机器学习和深度学习流程

    Fig.  6  Flow of traditional machine learning and deep learning

    图  7  航空发动机主要部件简图[135]

    Fig.  7  Sketch of main components of aero engine[135]

    图  8  航空发动机PdM的基本框架

    Fig.  8  Basic framework of PdM for aero engines

    表  1  预测信息

    Table  1  Prognostic information

    运行周期 真实RUL 时间窗口 1 (%) 时间窗口 2 (%) 时间窗口 3 (%)
    170 44 99.99 0.01 0
    180 34 99.91 0.09 0
    190 24 80.55 19.46 0.09
    200 14 0.02 76.96 23.02
    210 4 0 0 100
    下载: 导出CSV

    表  2  动态预测性维护方案

    Table  2  Dynamic predictive maintenance scenarios

    运行周期 真实 RUL 订货信号 存储信号 维护信号
    170 44 0 0 0
    180 34 0 0 0
    190 24 1 0 0
    200 14 0 0 0
    210 4 0 1 1
    下载: 导出CSV
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  • 收稿日期:  2020-04-20
  • 录用日期:  2020-09-07
  • 网络出版日期:  2021-01-29
  • 刊出日期:  2021-01-29

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