Decentralized Fault Detection System Design For Large-scale Interconnected System

Li, Wei; Gui, Weihua; Xie, Yongfang; Ding, Steven X.

doi:10.3182/20090630-4-es-2003.00134

Cited by 23 publications

(25 citation statements)

References 12 publications

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“…In fact, a centralized scheme sooner or later may hit one of the two following constraints on the hardware/software architecture used to implement it: limited available computation power for evaluating the fault decision, and limited communication bandwidth for acquiring all the necessary measurements. While considerable effort was aimed at developing distributed fault diagnosis algorithms suited to discrete event systems (see, for instance, [6]), much less attention was devoted to discrete or continuous-time systems (see [7], where the problem of designing sensor networks for fault-tolerant estimation is addressed, [8], [9] where faulttolerance in distributed systems is considered, [10], [11], [12], which are focused on decentralized fault detection, and [13] dealing with fault consensus in networks of unmanned vehicles).…”

Section: Introductionmentioning

confidence: 99%

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

Ferrari

Parisini

Polycarpou

2012

IEEE Trans. Automat. Contr.

207

205

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Abstract-This paper deals with the problem of designing a distributed fault detection and isolation methodology for nonlinear uncertain large-scale discrete-time dynamical systems. As a divide et impera approach is used to overcome the scalability issues of a centralized implementation, the large scale system being monitored is modelled as the interconnection of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem, a Local Fault Diagnoser is designed, based on the measured local state of the subsystem as well as the transmitted variables of neighboring states that define the subsystem interconnections. The local diagnostic decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive approximation of the interconnection with neighboring subsystems. The use of a specially-designed consensus-based estimator is proposed in order to improve the detectability and isolability of faults affecting variables shared among overlapping subsystems. Theoretical results are provided to characterize the detection and isolation capabilities of the proposed distributed scheme. Finally, simulation results are reported showing the effectiveness of the proposed distributed fault diagnosis methodology.

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Section: Introductionmentioning

confidence: 99%

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

Ferrari

Parisini

Polycarpou

2012

IEEE Trans. Automat. Contr.

207

205

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“…Reliability is a key requirement especially in these systems, as their increased size and complexity implies an increased risk of faults. When monitoring this kind of systems, the adoption of decentralized and distributed methods is usually necessary due to computational, communication, scalability and reliability limits (see [4], [5], [6], [7], [8] as examples). Moreover, an emerging requirement is the design of monitoring architectures that are robust to changes that may occur in the dynamic structure of the LSS.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Fault Detection in a plug-and-play scenario

Boem

Riverso

Ferrari-Trecate

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-This paper proposes a novel stochastic Fault Detection (FD) approach for the monitoring of Large-Scale Systems (LSSs) in a Plug-and-Play (PnP) scenario. The proposed architecture considers stochastic bounds on the measurement noises and modeling uncertainties, providing probabilistic timevarying FD thresholds with guaranteed false alarms probability levels. The monitored LSS consists of several interconnected subsystems and the designed FD architecture is able to manage plugging-in of novel subsystems and un-plugging of existing ones. Moreover, the proposed PnP approach can perform the unplugging of faulty subsystems in order to avoid the propagation of faults in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. The reconfiguration processes involve only local operations of neighboring subsystems, thus allowing a scalable architecture. A consensus approach is used for the estimation of variables shared among more than one subsystem; a method is proposed to define the time-varying consensus weights in order to allow PnP operations and to minimize at each step the variance of the uncertainty of the FD thresholds. Simulation results on a Power Network application show the effectiveness of the proposed approach.

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“…The interest towards Large-Scale Systems (LSSs) (see, for example, [1]), Systems-of-Systems [2] and CyberPhysical Systems [3], and their reliability requirements, is steadily growing both in industry and academia. When monitoring this kind of systems, the design of distributed or decentralized methods is usually necessary due to computational, communication, scalability and reliability limits (see [4], [5], [6], [7], [8], [9], [10], [11], as examples). We model these systems as a network of many subsystems connected through physical or communication interactions.…”

Section: Introductionmentioning

confidence: 99%

Distributed model-based fault diagnosis with stochastic uncertainties

Boem

Parisini

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-This paper proposes a novel distributed fault detection and isolation approach for the monitoring of non linear large-scale systems. The proposed architecture considers stochastic characterization of the measurement noises and modeling uncertainties, computing at each step stochastic timevarying thresholds with guaranteed false alarms probability levels. The convergence properties of the distributed estimation are demonstrated. A novel fault isolation method is proposed basing on a Generalized Observer Scheme, providing guaranteed error probabilities of the fault exclusion task. A consensus approach is used for the estimation of variables shared among more than one subsystem; a method is proposed to define the time-varying consensus weights in order to minimize at each step the variance of the uncertainty of the fault detection and isolation thresholds. Detectability and isolability conditions are provided.

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Decentralized Fault Detection System Design For Large-scale Interconnected System

Cited by 23 publications

References 12 publications

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

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

Stochastic Fault Detection in a plug-and-play scenario

Distributed model-based fault diagnosis with stochastic uncertainties

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