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

Keliris, Christodoulos; Polycarpou, Marios M.; Parisini, Thomas

doi:10.1080/00207179.2015.1007395

Cited by 34 publications

(19 citation statements)

References 33 publications

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“…In order to test the proposed stochastic PnP FDI architecture, we use the PNS example in Fig. 2 Σ [1] Σ [2] Σ [3] Σ [4] Σ [5] Σ [6] Σ [7] Σ [8] Σ [9] Σ [10] Σ [11] Σ [12] Σ [13] Σ [14] Σ [15] The weights on the edges represent P ij , the slope of the power angle curve at the initial operating angle between area i and area j. In Scenario 1, area 15 is initially disconnected and plugged-in at time 70s.…”

Section: Application Use Case: Power Networkmentioning

confidence: 99%

“…The interest towards LSSs [1], Systemsof-Systems (SoS) [2] and Cyber-Physical Systems (CPS) [3] is steadily growing both in industry and academia. When monitoring this kind of systems, distributed or decentralized algorithms are usually necessary due to computational, communication, scalability and reliability limits (see, among others, [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], and the references therein). Moreover, an emerging requirement is the design of monitoring architectures that are robust to changes that may occur in the dynamic topology of the LSS.…”

Section: Introductionmentioning

confidence: 99%

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

Boem

Riverso

Ferrari-Trecate

et al. 2019

IEEE Trans. Automat. Contr.

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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.

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Section: Application Use Case: Power Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Boem

Riverso

Ferrari-Trecate

et al. 2019

IEEE Trans. Automat. Contr.

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are some results that consider only the problem of FDI in networked and large‐scale systems using a distributed architecture only accounting for faults in local variables and propagation of their effect via distributed variables (see e.g., Refs. ). In Ref.…”

Section: Introductionmentioning

confidence: 97%

Distributed fault diagnosis of heating, ventilation, and air conditioning systems

et al. 2018

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The problem of distributed fault detection and isolation (FDI) for heating, ventilation, and air conditioning (HVAC) systems has been addressed in this work. First, a linear model is identified for subunits of an HVAC system. Next, a local FDI (LFDI) framework is designed for each unit under consideration. A distributed FDI architecture is designed where the LFDI frameworks communicate to exchange information to achieve enhanced FDI in each unit. As a result, each LFDI framework functions as intended even in the presence of faults that affect multiple units. Effectiveness of the proposed distributed FDI framework is shown for various commonly occurring fault scenarios. © 2018 American Institute of Chemical Engineers AIChE J, 65: 640–651, 2019

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“…In the last decade, there has been a significant research activity in the design of non-centralized FD architectures [Blanke et al (2016); Reppa et al (2016); Boem et al (2017); Keliris et al (2015)]. Decentralized and distributed methods are considered more suitable for large-scale applications than the centralized methods, especially with respect to scalability (e.g., installation of a new fan coil unit to an existing system) and security (e.g., a malicious attack of the central Building Management System).…”

Section: Introductionmentioning

confidence: 99%

Distributed Diagnosis of Actuator and Sensor Faults in HVAC Systems

2017

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This paper presents a model-based methodology for diagnosing actuator and sensor faults affecting the temperature dynamics of a multi-zone heating, ventilating and airconditioning (HVAC) system. By considering the temperature dynamics of the HVAC system as a network of interconnected subsystems, a distributed fault diagnosis architecture is proposed. For every subsystem, we design a monitoring agent that combines local and transmitted information from its neighboring agents in order to provide a decision on the type, number and location of the faults. The diagnosis process of each agent is realized in three steps. Firstly, the agent performs fault detection using a distributed nonlinear estimator. After the detection, the local fault identification is activated to infer the type of the fault using two distributed adaptive estimation schemes and a combinatorial decision logic. In order to distinguish between multiple local faults and propagated sensor faults, a distributed fault isolation is applied using the decisions of the neighboring agents. Simulation results of a 5-zone HVAC system are used to illustrate the effectiveness of the proposed methodology.

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

Cited by 34 publications

References 33 publications

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

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

Distributed fault diagnosis of heating, ventilation, and air conditioning systems

Distributed Diagnosis of Actuator and Sensor Faults in HVAC Systems

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