GOALI/Collaborative Research: Data-driven Statistical Prognosis and Service Decision Making for Teleservice Systems

本研究的目的是建立一系列的数据驱动的建模，故障预测和服务决策的方法，是为这两个机会和需求的企业服务系统的赠款机会与行业（GOALI）合作项目。在电力系统中，可以获得故障事件的离线历史记录和从大量机组中采集的状态监测信号。同时，对在役机组的状态监测信号也进行了真实的采集。这种前所未有的数据可用性为我们提供了重要的机会，可以开发准确而强大的算法来预测剩余使用寿命并做出最佳的服务决策。研究内容包括：（1）一种新的状态空间建模和非线性滤波方法，用于多相状态监测信号的建模和估计;（2）一个统一的框架，用于状态监测信号和故障时间数据的联合建模和故障预测;（3）基于联合预测模型的基于状态的预测服务策略;（4）基于状态的预测服务策略。及（iv）透过与通用汽车公司的合作而实施及验证。如果成功的话，这项研究的结果将增强企业服务系统的科学基础，并促进从反应/预防服务到基于模型的综合预测范式的转变。这项研究对于蓬勃发展的电子商务服务行业来说特别及时，帮助他们从基于经验的运营发展到高效的优化运营。鉴于信息爆炸和数据的无处不在，研究成果可以应用于广泛的产品，如制造系统和通信系统的服务。该项目的协同性质可以为学生提供独特的机会，以获得在各个领域的培训与汽车服务系统，包括可靠性，信号处理，车辆工程，统计和运筹学。

The research objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) collaborative project is to establish a series of data-driven modeling, failure prognosis, and service decision making methodologies that are tailored for both the opportunities and the needs of teleservice systems. In a teleservice system, the historical off-line records of failure events and the condition monitoring signals collected from a large number of units are available. At the same time, the condition monitoring signals from the in-service units are collected in real time as well. This unprecedented data availability provides us significant opportunities to develop accurate and robust algorithms to predict the remaining useful life and make optimal service decisions. The research consists of the following components: (i) a new state space formulation and nonlinear filtering approach for multi-phase condition monitoring signal modeling and estimation; (ii) a unified framework to jointly model the condition monitoring signals and the time-to-failure data for failure prognosis; (iii) a condition-based predictive service policy based on the joint prognosis model; and (iv) implementation and validation through collaboration with the General Motors. If successful, the results of this research will enhance the science base of teleservice systems and catalyze a transition from reactive/preventive service to an integrative model-based predictive paradigm. The research is particularly timely for the booming teleservice industry, helping them to evolve from experience-based operations into efficient optimized operations. Given the information explosion and the ubiquitous existences of data, the research results can be applied to the teleservice of a broad spectrum of products such as manufacturing systems and communication systems. The synergistic nature of this project can provide students the unique opportunity to obtain training in various fields related with teleservice systems, including reliability, signal processing, vehicle engineering, statistics, and operations research.