Jiawen Hu

University of Electronic Science and Technology of China, China

Title: Optimal Abort Policy for Mission-Critical Systems under Imperfect Condition Monitoring

---

Biography

Jiawen Hu is an Associate Professor at the School of Astronautics and Aeronautics, University of Electronic Science and Technology of China, Chengdu. He earned his B.S. in Mechanical Engineering from Shanghai Jiao Tong University in 2009, followed by an M.S. from the Chinese Academy of Sciences in 2012, and a Ph.D. in Industrial Engineering from Shanghai Jiao Tong University in 2017. From 2017 to 2020, he was a research fellow at the National University of Singapore. His research focuses on maintenance optimization and degradation modeling. His work has been published in leading journals such as Operations Research, Naval Research Logistics, IISE Transactions, and IEEE Transactions on Reliability.

Abstract

While most on-demand mission-critical systems are engineered to be reliable to support critical tasks, occasional failures may still occur during missions. To increase system survivability, a common practice is to abort the mission before an imminent failure. We consider optimal mission abort for a system whose deterioration follows a general three-state (normal, defective, failed) semi-Markov chain. The failure is assumed self-revealed, while the healthy and defective states have to be inferred from imperfect condition monitoring data. Due to the non-Markovian process dynamics, optimal mission abort for this partially observable system is an intractable stopping problem. For a tractable solution, we introduce a novel tool of Erlang mixtures to approximate non-exponential sojourn times in the semi-Markov chain. This allows us to approximate the original process by a surrogate continuous-time Markov chain whose optimal control policy can be solved through a partially observable Markov decision process (POMDP). We show that the POMDP optimal policies converge almost surely to the optimal abort decision rules when the Erlang rate parameter diverges. This implies that the expected cost by adopting the POMDP solution converges to the optimal expected cost. Next, we provide comprehensive structural results on the optimal policy of the surrogate POMDP. Based on the results, we develop a modified point-based value iteration algorithm to numerically solve the surrogate POMDP. We further consider mission abort in a multi-task setting where a system executes several tasks consecutively before a thorough inspection. Through a case study on an unmanned aerial vehicle, we demonstrate the capability of real-time implementation of our model, even when the condition-monitoring signals are generated with high frequency.