Proactive Maintenance: Integration of Engineering, Statistics, and Operations Research Towards a General Framework and Methodology

这笔赠款用于发展综合主动维修战略的科学基础。将研究各种基本问题，包括考虑到主动维护的全球目标的负担得起的过程中传感优化和验证；结合过程中数据和有限的历史信息进行退化建模、分析和预测；多属性决策；不确定性测量和风险分析；以及全球最优维护策略的确定和评估。这项研究将被组织成四个相互关联的任务：(1)优化使用负担得起的过程中传感的策略；(2)从多维观测中在线提取特征和传感器融合；(3)开发结合过程中传感数据、工程知识和有限的历史数据的预测故障模型；以及(4)开发用于评估主动维护策略的模拟试验台。其核心思想是开发一种方法，通过集成以下信息(考虑不确定性)来实现主动维护：(1)工程：过程中传感和在线建模、诊断和故障预测；(2)统计；可靠性模型和自我学习/更新；以及(3)管理科学：成本和风险分析、不确定性测量和性能评估。实施主动维护方法预计将显著改善制造设备和流程的维护，这将对制造质量、生产率和成本产生重大影响。研究项目的成功将克服不同学科之间的障碍，并展示主动维修综合方法的有效性，从而促进维修科学。此外，该研究还将为各个学科在以下领域的研究提供原创性贡献：(1)负担得起和优化的过程中感知和特征提取技术；(2)有限历史数据的可靠性分析；(3)用于维护策略改进的贝叶斯决策和自学习算法。该研究试验台将为未来主动维修的研究和示范奠定基础。

This grant provides for the development of a science base for the integrated proactive maintenance strategy. Various fundamental issues will be studied, which include affordable in-process sensing optimization and validation with consideration of the global goal of proactive maintenance; degradation modeling, analysis, and prediction by combining in-process data and limited historical information; multi attribute decision making; uncertainty measurement and risk analysis; and global optimal maintenance policy determination and evaluation. The research will be organized into four interrelated tasks: (1) Optimizing strategies for the use of affordable in-process sensing; (2) On-line feature extraction and sensor fusion from multi-dimensional observations; (3) Developing predictive failure models combining in-process sensing data, engineering knowledge, and limited historical data; and (4) Developing a simulation testbed for evaluation of proactive maintenance policy. The central idea is to develop a methodology to achieve proactive maintenance by integrating information (with uncertainty consideration) provided by: (1) Engineering: in-process sensing and on-line modeling, diagnosis, and failure prediction; (2) Statistics; reliability models and self-learning/updating; and (3) Management Science: cost and risk analysis; uncertainty measurement, and performance evaluation. The implementation of proactive maintenance methodologies are expected to significantly improve the maintenance of manufacturing equipment and processes, which has a significant impact on manufacturing quality, productivity, and cost. The success of the research project will contribute to maintenance science by overcoming the barriers among different disciplines, and demonstrating the effectiveness of the integrated methodology on proactive maintenance. In addition, the research will also provide original contribution to each individual discipline's research in areas such as (1) affordable and optimized in- process sensin g and feature extraction techniques; (2) reliability analysis with limited historical data; and (3) Bayesian decision-making and self-learning algorithms for maintenance policy improvements. The research testbed will serve as a basic for future research and demonstration of proactive maintenance.