Scalable monitoring of interconnected stochastic systems

Systems &Amp; Control: Foundations &Amp; Applications

Keliris

Parisini

et al. 2018

Self Cite

Fault diagnosis has been at the forefront of technological developments for several decades. Recent advances in many engineering fields have led to the networked interconnection of various systems. The increased complexity of modern systems leads to a larger number of sources of uncertainty which must be taken into consideration and addressed properly in the design of monitoring and fault diagnosis architectures. This chapter reviews a model-based distributed fault diagnosis approach for uncertain nonlinear large-scale networked systems to specifically address: a) the presence of measurement noise by devising a filtering scheme for dampening the effect of noise; b) the modeling of uncertainty by developing an adaptive learning scheme; c) the uncertainty issues emerging when considering networked systems, such as the presence of delays and packet dropouts in the communication networks. The proposed architecture considers in an integrated way the various components of complex distributed systems, such as: the physical environment, the sensor level, the fault diagnosers and the communication networks. Finally, some actions taken after the detection of a fault, such as the identification of the fault location and its magnitude or the learning of the fault function, are illustrated.

Section: Distributed and Networked Large-scale Systemsmentioning

confidence: 99%

Section: Distributed Fault Detectionmentioning

confidence: 99%

Fault Diagnosis for Uncertain Networked Systems

Systems &Amp; Control: Foundations &Amp; Applications

Keliris

Parisini

et al. 2018

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“…In this connection, in the recent paper [23], a fault detection and isolation method is proposed with probabilistic performance, but considering a centralized architecture. In [24], stochastic uncertainties are considered for a distributed FDI architecture, but in a completely different setting (non-overlapping models, linear dynamics, output measurements only, different assumptions on disturbances which require a distributed Kalman-like filtering scheme).…”

Section: Introductionmentioning

confidence: 99%

Plug-and-Play Fault Detection and Isolation for Large-Scale Nonlinear Systems With Stochastic Uncertainties

IEEE Trans. Automat. Contr.

Riverso

Ferrari-Trecate

et al. 2019

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This paper proposes a novel scalable model-based Fault Detection and Isolation approach for the monitoring of nonlinear Large-Scale Systems, consisting of a network of interconnected subsystems. The fault diagnosis architecture is designed to automatically manage the possible plug-in of novel subsystems and unplugging of existing ones. The reconfiguration procedure involves only local operations and communication with neighboring subsystems, thus yielding a distributed and scalable architecture. In particular, the proposed fault diagnosis methodology allows the unplugging of faulty subsystems in order to possibly avoid the propagation of faults in the interconnected Large-Scale System. Measurement and process uncertainties are characterized in a probabilistic way leading to the computation, at each time-step, of stochastic time-varying detection thresholds with guaranteed false-alarms probability levels. To achieve this goal, we develop a distributed state estimation scheme, using a consensus-like approach for the estimation of variables shared among more than one subsystem; the time-varying consensus weights are designed to allow plug-in and unplugging operations and to minimize the variance of the uncertainty of the fault diagnosis thresholds. Convergence results of the distributed estimation scheme are provided. A novel fault isolation method is then proposed, based on a Generalized Observer Scheme and providing guaranteed error probabilities of the fault exclusion task. Detectability and isolability conditions are provided. Simulation results on a power network model comprising 15 generation areas show the effectiveness of the proposed methodology.

“…1 Preliminary results have been presented in the very recent paper [1]. Compared with [1], a more comprehensive theoretical analysis is provided and extensive numerical results are given. For example, the conservativeness of the bound on the error covariance matrix is analyzed.…”

Section: Introductionmentioning

confidence: 99%

Distributed Fault Detection for Interconnected Large-Scale Systems: A Scalable Plug & Play Approach

IEEE Trans. Control Netw. Syst.

Carli

Farina

et al. 2019

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In this paper, we propose a novel distributed fault detection method to monitor the state of a-possibly largescale-linear system, partitioned into interconnected subsystems. The approach hinges on the definition of a partition-based distributed Luenberger-like estimator, based on the local model of the subsystems and that takes into account the dynamic coupling between the subsystems. The proposed methodology computesin a distributed way-a bound on the variance of a properly defined residual signal. This bound depends on the uncertainty affecting the state estimates computed by the neighboring subsystems and it allows the computation of local fault detection thresholds, as well as the maximum false-alarms rate. The implementation of the proposed estimation and fault detection method is scalable, allowing Plug & Play operations and the possibility to disconnect the faulty subsystem after fault detection. Theoretical conditions on the convergence properties of the estimates and of the estimation error bounds are provided. Simulation results on a power network benchmark show the effectiveness of the proposed method.