Signed Bond Graph for multiple faults diagnosis

Chatti, Nizar; Ould-Bouamama, Belkacem; Gehin, Anne-Lise; Merzouki, Rochdi

doi:10.1016/j.engappai.2014.07.018

Cited by 37 publications

(18 citation statements)

References 20 publications

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“…Further details on BG modeling and discussions on causality can be found in the literature. 16,45,46 BG for FDD BG has been successfully applied for FDD purposes. 15,16,23,46,47 It enables to generate fault indicators, called ARRs.…”

Section: Bg For Modelingmentioning

confidence: 99%

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Model-based fault detection and diagnosis of complex chemical processes: A case study of the Tennessee Eastman process

Tidriri

Chatti

Verron

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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Fault detection and diagnosis for industrial systems has been an important field of research during the past years. Among these systems, the Tennessee Eastman process is extensively used as a realistic benchmark to test and compare different fault detection and diagnosis strategies. In this context, data-driven approach has been widely applied for fault detection and diagnosis of the Tennessee Eastman process, by exploiting the massive amount of available measurement data. However, only few published works had attempted to deal with the dynamic behavior of the whole system including the mixing zone, circulating pumps, the reactor, the separator, the stripper, and so on, because of the difficulty of modeling physical phenomena that may occur in such complex system. In this article, an accurate model of the Tennessee Eastman process, properly tailored for fault detection and diagnosis purposes, is provided. This model shows better fault detection and diagnosis performances than all the others proposed in the literature and gives better or comparable results with the data-driven approaches. This work uses the bond graph methodology to systematically develop computational and graphical model. This methodology provides a physical understanding of the system and a description of its dynamic behavior. The bond graph model is then used for monitoring purposes by generating formal fault indicators, called residuals, and algorithms for fault detection and diagnosis. Hence, abnormal situations are detected by supervising the residuals' evolution and faults are isolated using the nature of the violated residuals. Therefore, the dynamic model of the Tennessee Eastman process can now be used as a basis to achieve accurately different analysis through the causal and structural features of the bond graph tool.

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Section: Bg For Modelingmentioning

confidence: 99%

“…This enables to consider them within the BG model as imposed signals and hence as starting point for eliminating the unknown variables, by substituting them using only measured and known ones. Details about this procedure can be found in the work by Chatti et al 16,23…”

Section: Bg Diagnosis Of the Tepmentioning

confidence: 99%

Model-based fault detection and diagnosis of complex chemical processes: A case study of the Tennessee Eastman process

Tidriri

Chatti

Verron

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

Self Cite

View full text Add to dashboard Cite

show abstract

“…In Ould‐Bouamama et al, the causal and structural properties of the BG model are used not only for system dynamics simulation but also to design FDI algorithms for online supervision of faults affecting sensors, actuators, and physicochemical components and phenomena. For details regarding BG‐based FDI, one is referred to the previous studies …”

Section: Problem Formulation and Related Workmentioning

confidence: 99%

“…For details regarding BG-based FDI, one is referred to the previous studies. [20][21][22] On the other hand, the PH formalism is especially of great importance regarding the modeling, control, and fault detection of systems due to many advantages, which explain why it is desired to preserve the particular structure. One of the strong aspects of the PH formalism is to expose the relations between the energy storage, dissipation, and interconnection structure; this framework underscores the physics of the system, and it ensures power conservative interconnections through the Dirac structures.…”

Section: Related Workmentioning

confidence: 99%

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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“…To improve diagnostic performance [23], it is necessary to determine the performance indices [24] (sensitivity index and fault detectability index). 78 | P a g e www.ijacsa.thesai.org by comparing it with the total energy provided by all the uncertainties.…”

Section: A Generation Of Performance Indicesmentioning

confidence: 99%

The Performance of the Bond Graph Approach for Diagnosing Electrical Systems

Mzoughi¹,

Sallami²,

Mami³

2017

ijacsa

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Abstract-The increasing complexity of automated industrial systems, the constraints of competitiveness in terms of cost of production and facility security have mobilized in the last years a large community of researchers to improve the monitoring and the diagnosis of this type of processes. This work proposes a reliable and efficient method for the diagnosis of an electrical system. The improvement of the reliability of the systems depends essentially on the algorithms of fault detection and isolation. The developed method is based on the use of analytical redundancy relations allowing the detection and isolation of faults which occur in the various elements of the system using a structural and causal analysis. In this context, the bond graph appears as an interesting approach since it models physical systems element by element which facilitates the detection and location of faults. The simulation of the system is performed through 20-sim software dedicated to the bond graph applications.

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Signed Bond Graph for multiple faults diagnosis

Cited by 37 publications

References 20 publications

Model-based fault detection and diagnosis of complex chemical processes: A case study of the Tennessee Eastman process

Model-based fault detection and diagnosis of complex chemical processes: A case study of the Tennessee Eastman process

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

The Performance of the Bond Graph Approach for Diagnosing Electrical Systems

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