A decentralized fault prognosis scheme for nonlinear interconnected discrete-time systems

Ferdowsi, Hasan; Raja, D.; Jagannathan, S.

doi:10.1109/acc.2012.6315478

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…with c i and˛i satisfying the conditions (7) and (9), R i . / satisfying conditions (26) and (27). Then, the result can be readily obtained based on Lemmas 5 and 6 given in Appendix 1.…”

Section: Definitionmentioning

confidence: 98%

“…The decentralized stabilization problems of large-scale systems with unknown interconnections have been studied by Xiong et al [28], Ma et al [29], and Meskin and Khorasani [33], whereas FDI is not covered. Considering time-invariant models, the decentralized FDI problems for nonlinear interconnected systems have been studied by Yan and Edwards [25], and Ferdowsi et al [26,27]. Unfortunately, little attention has been focused on Markovian Jump interconnected systems with unknown interconnections in the presence of process fault.…”

Section: Definition 1 ([8])mentioning

confidence: 99%

“…Taking this into account, a decentralized fault detection and estimation scheme that is robust to unknown interconnections has been established for a class of large‐scale interconnected systems based on the sliding mode technique . In , a decentralized FDI approach for nonlinear interconnected discrete‐time systems has been proposed based on online adaptive approximators. Further, this approach is extended to nonlinear interconnected continuous‐time systems .…”

Section: Introductionmentioning

confidence: 99%

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Decentralized fault detection and isolation of Markovian jump interconnected systems with unknown interconnections

Yang

2017

Intl J Robust & Nonlinear

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Summary This paper investigates the decentralized fault detection and isolation (FDI) problem for Markovian jump interconnected systems with unknown interconnections. Different from the existing decentralized FDI approaches, the requirement for access to operation modes of all subsystems, which is unreasonable and hard to meet in realistic applications, is removed. By utilizing local measurements and neighboring mode information, a decentralized FDI filter is constructed to generate a residual for each subsystem of Markovian jump interconnected system. Then, a new design method is developed such that the resulting augmented system is stochastically stable and the generated residual is sensitive to local fault. In addition, the proposed method can achieve fault detection and isolation simultaneously. Finally, two examples are given to illustrate the effectiveness and merits of the new results. Copyright © 2017 John Wiley & Sons, Ltd.

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“…with c i and˛i satisfying the conditions (7) and (9), R i . / satisfying conditions (26) and (27). Then, the result can be readily obtained based on Lemmas 5 and 6 given in Appendix 1.…”

Section: Definitionmentioning

confidence: 98%

Section: Definition 1 ([8])mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decentralized fault detection and isolation of Markovian jump interconnected systems with unknown interconnections

Yang

2017

Intl J Robust & Nonlinear

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“…Moreover the processing of the information at a centralized station imposes several risks since the station constitutes a single point of failure, thus making the architecture possibly fragile to faults. Recent ad-vances in communications and distributed sensing have allowed the transition from centralized fault diagnosis approaches [33,100,18,65,9] towards the development of hierarchical, decentralized and distributed schemes [66,54,102,56,108,89,15,84,29,31,48,49,96,90,76,52,75,55,8,40,23,78,14,13].…”

Section: Distributed and Networked Large-scale Systemsmentioning

confidence: 99%

Fault Diagnosis for Uncertain Networked Systems

Boem

Keliris

Parisini

et al. 2018

Systems &Amp; Control: Foundations &Amp; Applications

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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.

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“…In the last years though, due to advances in computing and communications mainly, the focus of the research activities is directed mostly towards the development of hierarchical (Klinkhieo & Patton, 2009; R. J. Patton et al, 2007), decentralized (Ferdowsi, Raja, & Jagannathan, 2012;Léchevin & Rabbath, 2009;Stankovic, Ilic, Djurovic, Stankovic, & Johansson, 2010;Wei, Gui, Xie, & Ding, 2009;X. Zhang, Polycarpou, & Parisini, 2009) and distributed (Boem, Ferrari, & Parisini, 2011;Ferrari, Parisini, & Polycarpou, 2012;Keliris, Polycarpou, & Parisini, 2013a;Yan, Tian, & Shi, 2008) schemes for process or sensor faults.…”

Section: Introductionmentioning

confidence: 99%

Distributed fault diagnosis for process and sensor faults in a class of interconnected input–output nonlinear discrete-time systems

Keliris

Polycarpou

Parisini

2015

International Journal of Control

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This paper presents a distributed fault diagnosis scheme able to deal with process and sensor faults in an integrated way for a class of interconnected input-output nonlinear uncertain discrete-time systems. A robust distributed fault detection scheme is designed, where each interconnected subsystem is monitored by its respective fault detection agent, and according to the decisions of these agents, further information regarding the type of the fault can be deduced. As it is shown, a process fault occurring in one subsystem can only be detected by its corresponding detection agent whereas a sensor fault in a subsystem can be detected by either its corresponding detection agent or the detection agent of another subsystem that is affected by the subsystem where the sensor fault occurred. This discriminating factor is exploited for the derivation of a high-level isolation scheme. Moreover, process and sensor fault detectability conditions characterizing quantitatively the class of detectable faults are derived. Finally, a simulation example is used to illustrate the effectiveness of the proposed distributed fault detection scheme.

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A decentralized fault prognosis scheme for nonlinear interconnected discrete-time systems

Cited by 10 publications

References 17 publications

Decentralized fault detection and isolation of Markovian jump interconnected systems with unknown interconnections

Decentralized fault detection and isolation of Markovian jump interconnected systems with unknown interconnections

Fault Diagnosis for Uncertain Networked Systems

Distributed fault diagnosis for process and sensor faults in a class of interconnected input–output nonlinear discrete-time systems

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