GOALI/Collaborative Research: Event-Log-Based Failure Prediction and Maintenance Service for After-Sales Engineering Systems

GOALI/协作研究：基于事件日志的售后工程系统故障预测和维护服务主要研究人员：Shiyu Zhou和Yong Chen摘要本合作研究项目的目标是测试系统事件日志是否包含足够的信息，以实现故障事件发生的统计合理和准确预测，如果是，建立通用的事件日志分析方法，用于故障预测和基于状态的售后工程系统的最佳维护。随着信息技术的飞速发展，系统中出现了大量记录事件的数据（例如，机器活动、关键系统故障、操作员/用户动作、任务状态）现在在系统使用时被自动收集。针对大量的事件日志，本研究主要包括四个部分：（1）利用事件日志拟合系统生存模型，量化各种系统事件与关键故障事件之间的关联性;（2）监测离散事件序列，统计检验由历史数据拟合的生存模型是否能充分代表当前系统特征;（3）基于生存模型和Semi-Markov决策过程的鲁棒条件服务策略;以及（4）在通用电气公司的医疗保健部门实施并验证所建立的医学成像诊断系统维护服务方法。如果成功，本研究将通过充分利用当前数据丰富的环境，推进售后设备维护服务操作的规划和控制方面的基础知识。本研究的成果将有助于售后服务行业从临时的经验型运营向高效的优化运营发展。此外，这个合作研究项目的跨学科性质可以为学生提供一个独特的机会来获得可靠性、运营研究、数据挖掘和统计方面的培训。鉴于系统事件日志的普遍存在，所建立的方法可能适用于更广泛的售后服务应用，如制造，通信和计算机网络系统。

GOALI/Collaborative Research: Event-Log-Based Failure Prediction and Maintenance Service for After-Sales Engineering SystemsPrinciple Investigators: Shiyu Zhou and Yong ChenABSTRACTThe 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.