Iterative distributed fault detection and isolation for linear systems based on moving horizon estimation

Abstract

Summary In modern engineering systems, reliability and safety can be conferred by efficient automatic monitoring and fault detection algorithms, allowing for the early identification and isolation of incipient faults. In case of large‐scale and complex systems, scalability issues and computational limitations make centralized monitoring and fault detection methods unapplicable. Research is therefore currently focusing on the development of distributed methods, where the computational complexity is divided among different units. In this paper, we propose a partition‐based model‐based fault detection and isolation scheme based on moving horizon estimation, able to estimate both the state variables and the possible faults, modeled as additive signals on the state and/or output equations. Its theoretical properties are analyzed, and numerical simulations are performed to witness its potentialities in a benchmark case study.

Distributed robust moving horizon estimation for multisensor systems with stochastic and norm‐bounded uncertainties

“…28 In this context, under distributed and decentralized approaches, several outcomes have been extended within the moving horizon filtering framework. 29 In the work of Zhang et al, 30 observer-enhanced distributed moving horizon filtering was investigated for large-scale nonlinear systems. Then, they developed this method for handling time-varying communication delays in Reference 31, and both data loss and communication delays in Reference 32.…”

Distributed cooperative fault detection and isolation for uncertain multi‐agent systems based on zonotope theory

“…1 Among the model-based FDI methods, observer-based FDI is actually one of the most effective strategies and many excellent research results can be found in literature. [2][3][4][5][6][7] For example, in Reference 3, a sliding mode-based FDI observer is presented using multiple-model scheme for a time invariant system with nonlinear perturbation. Based on unknown input observer, a sensor FDI method for complex power systems is proposed by Haes Alhelou et al 4 A multi-agent system (MAS) is a collection of several agents, working together and interacting with each other.…”

“…Generally speaking, model‐based FDI strategies include parameter identification‐based strategy, eigenstructure assignment‐based strategy, parity space‐based strategy, and observer‐based strategy 1 . Among the model‐based FDI methods, observer‐based FDI is actually one of the most effective strategies and many excellent research results can be found in literature 2‐7 . For example, in Reference 3, a sliding mode‐based FDI observer is presented using multiple‐model scheme for a time invariant system with nonlinear perturbation.…”

A Variational Bayes Moving Horizon Estimation Adaptive Filter with Guaranteed Stability

“…The main positive feature of MHE is the possibility of defining a performance criterion that can be designed specifically for the problem under consideration. Thanks to its guaranteed stability and performance (Alessandri et al, 2008;Rao et al, 2003), MHE has been widely used in both linear and nonlinear contexts (Alessandri and Awawdeh, 2016;Alessandri and Gaggero, 2017;Battistelli et al, 2017;Gharbi et al, 2021;Zou et al, 2020a) for centralized, networked, and distributed estimation (Battistelli, 2019;Farina et al, 2010a,b;Lauricella et al, 2020;Liu et al, 2013;Schneider and Marquardt, 2016;Yin and Liu, 2017;Zou et al, 2020b). The interested reader is referred to the special issue (Alessandri and Battistelli, 2020) for recent advances on MHE.…”