The objective of this collaborative research project is to test if the system event logs contain enough information to enable statistically sound and accurate prediction of the occurrence of failure events, and if yes, establish a generic event log analysis methodology for failure prediction and condition-based optimal maintenance of after-sales engineering systems. With the rapid development of information technology, an abundance of data that record the events occurred in a system (e.g., machine activities, critical system failures, operator/user actions, task status) are now collected automatically when the system is in use. Targeting the profusion of event logs, this research consists of four components: (1) fitting a system survival model using event logs to quantify the associations between various system events and the key failure event; (2) monitoring discrete events sequence to statistically test if the survival model fitted from historical data can fully represent the present system characteristics; (3) developing robust condition-based service policy based on the survival model and Semi-Markov decision processes; and (4) implementing and validating the established methodology for maintenance service of medical imaging diagnostic systems at the healthcare unit of General Electric company. If successful, this research will advance fundamental knowledge in the planning and control of maintenance service operations for after-sales equipment by fully exploiting the current data-rich environment. The results of this research will help after-sales service industry to evolve from ad hoc experience-based operations into efficient optimized operations. In addition, the interdisciplinary nature of this collaborative research project can provide students a unique opportunity to obtain training in reliability, operations research, data mining, and statistics. Given the ubiquitous existence of system event logs, the established methodologies are potentially applicable to a broader spectrum of after-sales service applications such as manufacturing, communication, and computer network systems.
