Latest Progress on Maintenance Strategy of Complex System: From Condition-based Maintenance to Predictive Maintenance
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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.
Abstract:Device maintenance is one of important and effective means to ensure the safety, reliability and availability of complex but repairable engineering systems. Maintenance strategy has experienced various phases including corrective maintenance (CM), time-based maintenance (TBM) and condition-based maintenance (CBM). Condition-based maintenance is the most attractive one in recent years. It can make in-time maintenance decisions by collecting and evaluating real-time condition monitoring information, hence it can be expected to achieve life-cycle high-reliability and low maintenance cost. Enabled by the internet of thing (IoT), advanced information and artificial intelligent (AI) technologies, a novel CBM strategy, predictive maintenance (PdM), is emerging and gaining increasing attentions. This paper firstly reviews the main development history of maintenance strategy and then focuses on the latest progress of CBM. According to the differences of decision-support approaches, CBM strategies are divided into stochastic deterioration model based CBM and data-driven PdM. It should be noted that, PdM can be regarded as an extension of CBM. After that, development branches and research status of each approach are sorted out and summarized. Finally, the challenging problems and possible future research directions are discussed.
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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 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
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 
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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
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