Distributed Fault Detection and Isolation of Large-Scale Discrete-Time Nonlinear Systems: An Adaptive Approximation Approach

Ferrari, Riccardo; Parisini, Thomas; Polycarpou, Marios M.

doi:10.1109/tac.2011.2164734

Cited by 207 publications

(211 citation statements)

References 41 publications

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“…In this section, the extension of the deterministic consensus mechanism illustrated in Ferrari et al (2012) to a stochastic context is illustrated. In this case, the I-th estimation model iŝ…”

Section: Consensus-based Fd Methodologymentioning

confidence: 99%

“…, N . Let S be the set of the variables shared among more than one subsystem and d k be the overlap degree (Ferrari et al (2012)) of the k-th state variable, k = 1, . .…”

Section: Problem Formulationmentioning

confidence: 99%

“…Recently there has been a growing interest towards distributed architectures for fault diagnosis of large-scale and networked systems (see, for instance Boem et al (2011);Ferrari et al (2012); Zhang and Zhang (2012); Boem et al (2013a,b); Keliris et al (2015); Reppa et al (2015)). As it is well known, the drawbacks of a centralized fault diagnosis architecture are scalability and robustness.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the works proposing distributed monitoring schemes for discretetime or continuous-time systems, like Boem et al (2011);Ferrari et al (2012); Zhang and Zhang (2012), assume that the decomposition of the system into subsystems is given a priori. The aim of this work is to study the decomposition problem specifically for the fault detection task.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Topology for Distributed Fault Detection of Large-scale Systems ★ ★This paper has been partially supported by the EPSRC STABLENET grant EP/L014343/1.

et al. 2015

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The paper deals with the problem of defining the optimal topology for a distributed fault detection architecture for non-linear large-scale systems. A stochastic modelbased framework for diagnosis is formulated. The system structural graph is decomposed into subsystems and each subsystem is monitored by one local diagnoser. It is shown that overlapping of subsystems allows to improve the detectability properties of the monitoring architecture. Based on this theoretical result, an optimal decomposition design method is proposed, able to define the minimum number of detection units needed to guarantee the detectability of certain faults while minimizing the communication costs subject to some computation cost constraints. An algorithmic procedure is presented to solve the proposed optimal decomposition problem. Preliminary simulation results show the potential of the proposed approach.

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Section: Consensus-based Fd Methodologymentioning

confidence: 99%

“…, N . Let S be the set of the variables shared among more than one subsystem and d k be the overlap degree (Ferrari et al (2012)) of the k-th state variable, k = 1, . .…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Topology for Distributed Fault Detection of Large-scale Systems ★ ★This paper has been partially supported by the EPSRC STABLENET grant EP/L014343/1.

et al. 2015

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“…In Ferrari et al (2012), a Distributed Fault Detection and Isolation (DFDI) methodology is proposed, consisting of N agents called Local Fault Diagnosers (LFDs) L I , I ∈ {1 . .…”

Section: Distributed Detection Architecturementioning

confidence: 99%

Distributed Fault Diagnosis for Input-Output Continuous-Time Nonlinear Systems

Boem

Ferrari

Parisini

et al. 2012

IFAC Proceedings Volumes

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In this paper, new results on distributed fault diagnosis of continuous-time nonlinear systems with partial state measurements are proposed. Following an overlapping decomposition framework, the dynamics of a nonlinear uncertain large-scale dynamical systems is described as the interconnection of several subsystems. Each subsystem is monitored by its own Local Fault Diagnoser, based on a set of local estimators. A consensus-based protocol is used to improve the detectability and the isolability of faults affecting variables shared among different subsystems because of the overlapping decomposition. A sufficient condition assuring the convergence of the estimation errors is derived. Time-varying threshold functions guaranteeing no false-positive alarms and theoretical results containing detectability and isolability conditions are presented.

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Sparse set membership identification of nonlinear functions and application to fault detection

Novara

2015

Adaptive Control & Signal

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The problem of approximating an unknown function from data and deriving reliable interval estimates is important in many fields of science and technology. In this paper, an algorithm is proposed to solve this problem, based on a sparsification technique and a nonparametric set membership analysis. Assuming that the noise affecting the data is bounded and the unknown function satisfies a mild regularity assumption, it is shown that the algorithm provides an approximation with suitable optimality properties, together with tight interval estimates. An innovative approach to fault detection, based on the derived interval estimates, is then proposed, overcoming some relevant problems proper of the 'classical' techniques. The approach is applied in a simulation study to solve the challenging problem of fault detection for a new class of wind energy generators, which uses kites to capture the power from high-altitude winds.

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Distributed Fault Detection and Isolation of Large-Scale Discrete-Time Nonlinear Systems: An Adaptive Approximation Approach

Cited by 207 publications

References 41 publications

Optimal Topology for Distributed Fault Detection of Large-scale Systems ★ ★This paper has been partially supported by the EPSRC STABLENET grant EP/L014343/1.

Optimal Topology for Distributed Fault Detection of Large-scale Systems ★ ★This paper has been partially supported by the EPSRC STABLENET grant EP/L014343/1.

Distributed Fault Diagnosis for Input-Output Continuous-Time Nonlinear Systems

Sparse set membership identification of nonlinear functions and application to fault detection

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