基于多准则场景缩减的“零停机”设备状态预测与维护方法研究

项目名称： 基于多准则场景缩减的“零停机”设备状态预测与维护方法研究

项目编号： No.71501148

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 管理科学

项目作者： 钱新博

作者单位： 武汉科技大学

项目金额： 17.4万元

中文摘要： 设备运行时，若发生非计划故障停机，会产生较大经济损失，严重时会导致连锁灾难性事故。为保证设备连续可靠运行、实现“零停机”，需分析具有不确定的大监测数据，预测设备状态演变趋势，制定设备维护策略。.为此，提出以多准则场景缩减为核心，包括场景构建、场景缩减、维护决策三层次的“零停机”设备状态预测与维护方法，主要包括：①抽取状态特征，提出基于状态空间和时间尺度的双向变步长最优离散方法，构造涵盖全部不确定情形的场景集；②利用设备可靠性和维护成本生成目标函数，挖掘场景特征与目标函数间的关联规则，研制能保证预测精度和缩减效率、融合概率测度与关联规则两类准则的场景缩减机制；③利用前向选择法生成场景树，基于时序逻辑构造多时段设备维护模型，获得稳定性强、可靠性高的状态维护方案；④将研究成果用于水轮机水导轴承、轧机轧辊轴承等关键设备，展开实例验证。.本研究有利于丰富和发展设备健康管理理论，提高设备运行维护水平。

中文关键词： “零停机”设备；健康管理；状态预测；状态维护；场景缩减

英文摘要： The unscheduled downtime of any equipment in operation may cause enormous economic losses and even cascading disasters. To improve continuity and reliability of equipments and eliminate unscheduled downtime, massive monitoring data under uncertainties need to be processed for condition-based prediction and then condition-based maintenance. .Focusing on multi-criteria scenario reduction, this project proposes a methodology for condition-based prediction and maintenance of mechanical equipment without unscheduled downtime including three layers, scenario construction, scenario reduction and maintenance decision-making. This project comprises four sections as follows. .① First, on the ground of monitoring data, all values of condition-based features are discretized with variable step-size in temporal and status scales, so as to generate the initial set of all possible scenarios. .② Then, association rules between scenario features and maintenance objectives are extracted using data mining technique, multi-criteria scenario reduction mechanism is developed based on probability measure and association rules, and a reduced scenario set with allowable solution errors and low computational complexity is obtained. .③ Scenario tree is formed based on the selected scenario set by forward selection heuristic. Condition-based maintenance strategy is deduced with balanced stability and reliability. .④Finally, utilizing condition-based prediction and maintenance methodology via multi-criteria scenario reduction techniques, guide bearing of water turbine and roller bearing of rolling mill are both hired to further testify the proposed methodology. .This research will enrich basic theory in health management of equipment, and promote operation and maintenance level of mechanical equipment.

英文关键词： Mechanical equipment without unscheduled downtime;Health management;Condition-based prediction;Condition-based maintenance;Scenario reduction