Self-cognizant Prognostics for Electronics-rich Systems

Description

Current methods for reliability assessment of electronics-rich systems have fundamental flaws due to their inability to keep pace with new technologies, to account for complex usage profiles, and to address soft and intermittent faults (the most common failure modes in today’s electronics-rich systems). This is especially problematic given the fact that these systems do commonly fail (e.g., airplane crashes caused by failed electronics, LED outdoor lighting systems that cannot survive two months, a host of automotive engine controller failures, electric grid shutdowns caused by failed sensors, and financial shut-downs due to failed servers). The impact on safety, availability and cost is staggering.There is a strong need for a major change in system health (degradation) assessment, fault diagnostics, and prognostics (the real-time prediction of reliability and the remaining useful life) of electronics-rich systems. There is also a special need to address soft faults and intermittent failures. Thus, the proposed team effort is a radically new approach whose research goals include effective and efficient reliability prognostics and health management (PHM) for electronics-rich systems that continuously monitor themselves (are self-cognizant) using algorithms that fuse sensor data, discriminate transient (intermittent) and false alarms from actual failures, correlate faults with relevant system events and mode changes, and predict failures in advance. In particular, we will:‧ Develop a prognostics and health management test laboratory where soft and intermittent failures can be generated and methods tested. This will be a unique world-class laboratory.‧ Develop fault identification and prognostics technologies and software, which use a fusion of advanced symbolic-time series analysis, optimal feature selection and physics-of-failure analysis to predict system degradation, malfunctions and remaining useful life of critical electronics-rich systems, and to assist in root-cause failure analysis.‧ Demonstrate our methods and algorithms using simulated data (derived from previous experimental data from our team), experimental data from our test lab, and field data from our team and collaborators.‧ Conduct applied engineering research with our team partners to address the impact of PHM implementation on business concerns, warranty issues, and return on investment.The results of this research will revolutionize the field of reliability for electronics and specifically benefit the Hong Kong and Pearl River Delta region in their new development of LED lighting infrastructure, avionics and next generation vehicles.