Supervision of an industrial steam generator. Part I: Bond graph modelling

Bouamama, Belkacem Ould; Medjaher, Kamal; Samantaray, Arun Kumar; Staroswiecki, Μ.

doi:10.1016/j.conengprac.2005.01.004

Cited by 89 publications

(54 citation statements)

References 13 publications

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“…By using the FDI algorithms proposed in [4,1], a list of Analytical Redundancy Relation (ARR) along with the corresponding Fault Signature Matrix (FSM) can be generated. These tools allow to detect and isolate the possible faults present on the physical system.…”

Section: Backlash Torque Detectionmentioning

confidence: 99%

“…In this part, a model based Fault Detection and Isolation (FDI) approach [4,11,5] is used in order to detect a undesirable dead zone and disturbing backlash torque in an electromechanical system in presence of wear phenomenon. This detection can help on improving the system performances by developing adaptive control laws [7].…”

Section: Backlash Torque Detectionmentioning

confidence: 99%

“…Furthermore, with structural analysis method, the elimination of unknown variables from the constraints is not trivial for complex systems. However, by using bond graphs, it is well known that for an observable system there are as many residuals as number of sensors present on the physical system [4]. So, once the junction equations are written, the elimination procedure is straight forward by using the causal paths from the known to unknown variables on the bond graph model, used in differential causality.…”

Section: Introductionmentioning

confidence: 99%

“…The main steps to generate the list of ARR and the FSM by using the method developed in [4] are summarized bellow:…”

mentioning

confidence: 99%

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Backlash fault detection in mechatronic system

et al. 2007

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In this paper, a fault detection and isolation model based method for backlash phenomenon is presented. The aim of this contribution is to be able to detect then distinguish the undesirable backlash from the useful one inside an electromechanical test bench. The dynamic model of the real system is derived, using the bond graph approach, motivated by the multi-energy domain of such mechatronic system. The innovation interest of the use of the bond graph tool, resides in the exploitation of one language representation for modelling and monitoring the system with presence of mechanical faults. Fault indicators are deduced from the analytical model and used to detect and isolate undesirable backlash fault, including the physical system. Simulation and experimental tests are done on electromechanical test bench which consists of a DC motor carrying a mechanical load, through a reducer part containing a backlash phenomenon.

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Section: Backlash Torque Detectionmentioning

confidence: 99%

Section: Backlash Torque Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The main steps to generate the list of ARR and the FSM by using the method developed in [4] are summarized bellow:…”

mentioning

confidence: 99%

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Backlash fault detection in mechatronic system

et al. 2007

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“…It was used therein to model the heat transfer from the external environment to the inside of a building. Many authors have worked on the bond graph modelling of thermo-fluidic systems: general modelling framework development, thermo-fluid library for process engineering systems (Samantaray et al, 2004), multi-phase systems (Brown, 2002) and applications to steam generators (Ould Bouamama et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Thermal modelling, simulation and experimental validation of heat accumulation in a framed glass cabin

Bera

Dixit

Bhattacharya

et al. 2017

jtam

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The present work concerns prediction of the amount of heat accumulation within the interior of a framed glass cabin and proposes some remedial measures to reduce temperature in the cabin. Various configurations such as double layer glass box filled with static air, static argon gas and flowing argon gas within the space between the two glass-layers are considered to conduct the experiments. Multi-physics bond graph models for these configurations are developed considering thermo-fluidic aspects. The experimental results are compared with the simulations using bond graph models. Though, direct application to a vehicle is not made, without loss of generality, the modelling and experimental procedure can be extended to analyze the heat accumulation inside a vehicle cabin when the vehicle is parked under direct sunlight.

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A comparative study of energetic model-based fault detection using HBG and COG formalisms

Atitallah

Harabi

Abdelkrim

2017

Int Trans Electr Energ Syst

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Summary The problem of modeling and fault detection in an electromechanical system having a graphical representation is considered. For this system's dynamics, motivated by a desire to provide a precise fault detection procedure using a unified energy‐based framework, 2 energy‐based graphical formalisms are presented and compared: the Hamiltonian Bond Graph and the Causal Ordering Graph. Firstly, easily calculated from the Hamiltonian Bond Graph representation covering the causal energetic paths, the energetic residual generators are systematically deduced by comparing the energy quantity in both normal and faulty situations. Secondly, the causal and structural properties of the Causal Ordering Graph tool can be used to design an observer devoted to fault detection so as to derive directly energy‐based residual generators in terms of faults. To highlight the effectiveness and applicability of 2 proposed approaches, simulation results on DC motor are provided and discussed. The performances of these fault detection schemes are compared and discussed in detail with respect to existing methods.

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Supervision of an industrial steam generator. Part I: Bond graph modelling

Cited by 89 publications

References 13 publications

Backlash fault detection in mechatronic system

Backlash fault detection in mechatronic system

Thermal modelling, simulation and experimental validation of heat accumulation in a framed glass cabin

A comparative study of energetic model-based fault detection using HBG and COG formalisms

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