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

GOALI合作项目的研究目标是建立一系列数据驱动的建模、故障预测和服务决策方法，为远程服务系统的机会和需求量身定做。在远程服务系统中，可以获得故障事件的历史离线记录和从大量单元收集的状态监测信号。同时，还实时采集来自在役单元的状态监测信号。这种前所未有的数据可用性为我们提供了开发准确且强大的算法来预测剩余使用寿命并做出最佳服务决策的重要机会。研究包括以下几个部分：(I)用于多阶段状态监测信号建模和估计的新的状态空间描述和非线性滤波方法；(Ii)用于故障预测的状态监测信号和故障间隔时间数据的联合建模的统一框架；(Iii)基于联合预测模型的基于状态的预测服务策略；以及(Iv)通过与通用汽车的合作实施和验证。如果成功，这项研究的结果将增强远程服务系统的科学基础，并催化从反应性/预防性服务向基于模型的综合预测范例的过渡。对于蓬勃发展的远程服务行业来说，这项研究尤其及时，有助于他们从基于经验的运营演变为高效的优化运营。在信息爆炸和数据无处不在的情况下，研究成果可以应用于制造系统和通信系统等广泛领域的远程服务。该项目的协同性可以为学生提供获得与远程服务系统相关的各个领域的培训的独特机会，包括可靠性、信号处理、车辆工程、统计学和运筹学。

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.