Sriram Narasimhan

TL;DR: This paper presents a methodology for online tracking and diagnosis of hybrid systems and demonstrates the effectiveness of the approach with experiments conducted on the fuel-transfer system of fighter aircraft.

TL;DR: In this paper, the authors present a methodology for online tracking and diagnosis of hybrid systems that combine digital (discrete) supervisory controllers with analog (continuous) plants, and demonstrate the effectiveness of the approach with experiments conducted on the fuel transfer system of fighter aircraft.

TL;DR: This work is the first research utilizing the route Problem → QUBO → Direct embedding into quantum hardware, where it is able to implement and tackle problem instances with sizes that go beyond previously reported toy-model proof-of-principle quantum annealing implementations; this is a significant leap in the solution of problems via direct-embedding adiabatic quantum optimization.

TL;DR: In this paper, a robust diagnosis methodology for hybrid systems that combines fault detection with a combined qualitative and quantitative fault isolation scheme is presented. But the authors focus on fault detection, symbol generation, and parameter estimation, and illustrate the effectiveness of this method by running experiments on the fuel transfer system of aircraft.

TL;DR: This paper presents an approach that combines the analytic modelbased and featuredriven diagnosis approaches, the analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults.