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.
