A set based probabilistic approach to threshold design for optimal fault detection

A set based probabilistic approach to threshold design for optimal fault detection

Rostampour, Vahab (TU Delft Networked Cyber-Physical Systems)
Ferrari, Riccardo M.G. (TU Delft Data-Driven Control)
Keviczky, T. (TU Delft Networked Cyber-Physical Systems)

Sun, J. (editor)
Jiang, Z.-P. (editor)

2017

Traditional deterministic robust fault detection threshold designs, such as the norm-based or limit-checking method, are plagued by high conservativeness, which leads to poor fault detection performance. On one side they are ill-suited at tightly bounding the healthy residuals of uncertain nonlinear systems, as such residuals can take values in arbitrarily shaped, possibly non-convex regions. On the other hand, they must be robust even to worst-case, rare values of the modeling and measurement uncertainties. In order to maximize performance of detection, we propose two innovative ideas. First, we introduce threshold sets, parametrized in a way to bound arbitrarily well the residuals produced in healthy condition by an observer-based residual generator. Secondly, we formulate a chance-constrained cascade optimization problem to determine such a set, leading to optimal detection performance of a given class of faults, while guaranteeing robustness in a probabilistic sense. We then provide a computationally tractable framework by using randomization techniques, and a simulation analysis where a well-known three-tank benchmark system is considered.

http://resolver.tudelft.nl/uuid:b92ee895-a2a4-40a6-b39e-eb07330e6448

https://doi.org/10.23919/ACC.2017.7963798

IEEE, Piscataway, NJ, USA

978-1-5090-5992-8

Proceedings of the 2017 American Control Conference (ACC 2017)

2017 American Control Conference, ACC 2017, 2017-05-24 → 2017-05-26, Seattle, United States

Accepted Author Manuscript

Institutional Repository

conference paper

© 2017 Vahab Rostampour, Riccardo M.G. Ferrari, T. Keviczky