Supervision of a Three Tanks System by Bond Graph and External Models

Sallami, Abderrahmene; Zanzouri, Nadia; Ksouri, Mekki; Bouamama, Belkacem Ould

doi:10.1109/ccca.2011.6031223

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…The nominal values are grouped together in an augmented matrix denoted M, supposed to be proper, and the uncertainties whatever their type (structured and unstructured parametric uncertainties, modeling uncertainties, measurement noises, etc.) are combined in a matrix Δ of structure diagonal shown in figure 2 [38,40].…”

Section: Robust Supervision Systems Using the Bond Graphmentioning

confidence: 99%

“…For the external model, the execution of a service requires the availability of a certain number of resources, and is triggered only after the verification of an activation condition. Although all the resources required to run a service are in perfect working order, it may be impossible to perform the service [12,16]. However, the failure of certain resources does not imply the unavailability of the service that uses them.…”

Section: Introductionmentioning

confidence: 99%

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Robust Supervision by Integration External and Bond Graph Models of a Desalination System

Sallami¹,

Mzoughi²,

Allegui³

et al. 2019

Preprint

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This article aims to solve the problem of robust supervision of a reverse osmosis desalination system (RO-DS) with two models, external model and bond graph model. The structure of an industrial system from the point of view of the external model operates according to several modes of operation (degraded and normal). For this external model, we cannot locate the faults since we are talking about a global operation of the system. The possible solution for the development of research was to use the multidisciplinary model called bond graph. This model by its graphic nature and using a unified language makes it possible to model the industrial system element by element from where it helps the user not only to detect the faults but also to locate them when they appear in the system. The results suggest that the use of the bond graph model for alarm 02 is a reverse osmosis error (RO1); these phenomena are readable on the bond graph model and can be quantified by equations. The equation of the model of this reverse osmosis (RO1) is found in the residual equations (r5, r6 and r7), so that these residues will be sensitive to this rupture. The possible solution during research development was to use the bond graph model to supplement information with physical knowledge and to locate faults. This article also describes the operating safety (by minimizing false alarms and non-detections as well as delays in fault detection) of the desalination system by using the bond graph model described in Linear Fractional Transformation (LFT) form for enable it to manage the robust supervision of a desalination system. An approach based on (LFT-BG) is developed to monitor tank leakage (Cu) and reverse osmosis (RO1 and RO2) faults or valve level (V1 and V2) closures and membrane clogging reverse osmosis (Rm1 and Rm2) that can occur in the reverse osmosis desalination system (RO-DS).

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Section: Robust Supervision Systems Using the Bond Graphmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Supervision by Integration External and Bond Graph Models of a Desalination System

Sallami¹,

Mzoughi²,

Allegui³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, supervisory systems use intelligent systems that provide the user with assistance in managing their urgent alarm tasks in order to increase the reliability and dependability of processes [5][6][7][8][9][10]. However, the improvement of the dependability of the systems is essentially based on fault detection and isolation algorithms [11][12][13][14][15][16][17][18][19], these algorithms have the role of comparing the actual behavior of the system wither ferrous behaviors describing the normal operation. The degradation of the performance of diagnostic algorithms is mainly due to the imperfect knowledge of the parametric values of the models and to their random variations [20].…”

Section: Introductionmentioning

confidence: 99%

Efficiency of bond graph and external model integration for alarm processing of a central air conditioning system

Sallami

Mzoughi

Allagui

et al. 2020

IJAAS

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<p><span>The design of a supervision system based on the external model by structuring the industrial process according to several modes of operation (degraded and normal). The disadvantage of this model is that it describes the industrial process components as functions regardless of their dynamics without going into detail. Hence the interest of the bond graph model to fill the external model limits. The performance of the proposed supervisory system using both models lies in the detection and location of faults for each mode of operation. The bond graph model enriched by the concept of causality and thanks to these structural properties can clearly display the elements of the physical system taking into account their dynamics in normal and abnormal operation. The results of our research have been applied to central air conditioning system; the development of the proposed project has proceeded from the modeling stage to the reconfiguration stage of the system.</span></p>

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“…This model includes a number of parameters whose values are assumed to be known during normal operation. The comparison between the actual behavior of the system and the expected behavior given by the model provides a quantity called the residue that will be used to determine whether the system is in a failing state and to specify the part or component of the system faulty system [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Robust Diagnosis of Vehicular Active Suspension System Using Bond Graph Approach

Sellami¹,

Mzoughi²,

Mami³

2018

IJET

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Diagnostic systems play a major role in the safety of industrial systems and the availability of its equipment. Rapid detecti on (as soon as possible) to the operator of the detected deviations (defects) in relation to the expected nominal behavior is fundamental for the implementation of preventive and corrective actions on industrial systems. These industrial systems are governed by several physical phenomena and various technological components, which is why the Bond Graph tool, based on an energy and multi-physical analysis, is well suited. In this article, we will discuss the problem of diagnosing an active suspension system of a vehicle by presenting diagnostic methods. Then, we introduce the bond graph tool for the robust diagnosis of the system. Finally, the extension of the deterministic models presented to models integrating uncertain element s (Fractional linear transformations LFT) and the generation of robust analytical redundancy relationships are also detailed.

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Supervision of a Three Tanks System by Bond Graph and External Models

Cited by 3 publications

References 4 publications

Robust Supervision by Integration External and Bond Graph Models of a Desalination System

Robust Supervision by Integration External and Bond Graph Models of a Desalination System

Efficiency of bond graph and external model integration for alarm processing of a central air conditioning system

Robust Diagnosis of Vehicular Active Suspension System Using Bond Graph Approach

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