Load allocation for optimal risk management in systems with incipient failure modes

System health management;Fault risk mitigation;Failure prognostics;Finite horizon prognostics;Markov decision process;Battery discharge prediction

Bole, Brian McCaslyn ; Taylor, David G. Vachtsevanos, George Electrical and Computer Engineering Michaels, Thomas E. Habetler, Thomas G. Goebel, Kai ; Taylor, David G.

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: System health management;    Fault risk mitigation;    Failure prognostics;    Finite horizon prognostics;    Markov decision process;    Battery discharge prediction;   

Others  :  https://smartech.gatech.edu/bitstream/1853/50394/1/BOLE-DISSERTATION-2013.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The development and implementation challenges associated with a proposed load allocation paradigm for fault risk assessment and system health management based on uncertain fault diagnostic and failure prognostic information are investigated. Health management actions are formulatedin terms of a value associated with improving system reliability, and a cost associated with inducing deviations from a system's nominal performance. Three simulated case study systems are considered to highlight some of the fundamental challenges of formulating and solving an optimization on the space of available supervisory control actions in the described health management architecture. Repeated simulation studies on the three case-study systems are used to illustrate an empirical approach for tuning the conservatism of health management policies by way of adjusting risk assessment metrics in the proposed health management paradigm. The implementation and testing of a real-world prognostic system is presented to illustrate model development challenges not directly addressed in the analysis of the simulated case study systems. Real-time battery charge depletion prediction for a small unmanned aerial vehicle is considered in the real-world case study. An architecture for offline testing of prognostics and decision making algorithms is explained to facilitate empirical tuning of risk assessment metrics and health management policies, as was demonstrated for the three simulated case study systems.