Merging Bond Graph and Signed Directed Graph to improve FDI procedure

The Fuel Cell (FC) is an ideal electrical powersource. However, FC stacks and even more FC systems are vulnerable to faults (such as water flooding and membrane drying) that can cause the disruption or the permanent damage. To guarantee the safe operation of the FC systems, it is necessary to use systematic techniques to detect and isolate faults for the purpose of diagnosis. The problematic for the model-based Fault Detection and Isolation (FDI) of fuel cell is that the model is complex because of coupling multiple physical domains (electrochemical, electrical, thermofluidic...). This is why, we propose in this paper, the exploitation of the behavioral and structural properties of the Bond Graph (BG) as a multi-domain power exchange and unified graphical modeling language for qualitative analysis of monitoring ability (using Signed Directed Graph properties). This is obtained after generation of the fault indicators from one part, and by dealing with an automatically built Signed Directed Graph (SDG) of the system, from another part. By combining qualitative method (based on Signed Graph) and quantitative method (fault indicator generation) using only one representation, an innovative approach to perform (single and multiple faults) diagnosis is proposed. The proposed contribution is illustrated by an application to a Proton Exchange Membrane Fuel Cell (PEMFC)

Robust fault decision : Contribution to Omni directional Mobile Robot

Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems and avoiding the execution of an unsafe behaviour. This chapter deals with Robust Decision Making (RDM) for fault detection of electromechanical systems by combining the advantages of Bond Graph (BG) modeling and Fuzzy logic reasoning. A fault diagnosis method implemented in two stages is proposed. In the first stage, the residuals are deduced from the BG model allowing the building of a Fault Signature Matrix (FSM) according to the sensitivity of residuals to different parameters. In the second stage, the result of FSM and the robust residual thresholds are used by the fuzzy reasoning mechanism in order to evaluate a degree of detectability for each set of components. Finally, in order to make robust decision according to the detected fault component, an analysis is done between the output variables of the fuzzy system and components having the same signature in the FSM. The performance of the proposed fault diagnosis methodology is demonstrated through experimental data of an omni directional robot. - See more at: http://www.eurekaselect.com/102039/chapter/robust-fault-decision%3A-appl...

SBG for Health Monitoring of Fuel Cell System

To guarantee the safe operation of the Fuel Cell (FC) systems, it is necessary to use systematic techniques to detect and isolate faults for diagnosis purposes. The problematic for Fault Detection and Isolation (FDI) model-based of fuel cell consists in that such system is bad instrumented, its model is complex (because of coupling of multi-physical phenomena such as electrochemical, electrical, thermo fluidic…) and the numerical values related to it are not always known. This is why qualitative model (based on existence or not of the links between variables and the relations) is well suited for fuel cell diagnosis. In this paper, we propose a new graphical model (named Signed Bond Graph) allowing to combine both qualitative and quantitative features for health monitoring (in terms of diagnosis and prognosis) of the fuel cell. The innovative interest of the presented paper is the use of only one representation for not only structural model but also diagnosis of faults which may affect the fuel cell. The developed theory is illustrated by an application to a Proton Exchange Membrane Fuel Cell (PEMFC).

A Bond Graph Modeling for Health Monitoring and Diagnosis of the Tennessee Eastman Process

Data-driven fault detection and diagnosis approaches are widely applicable in many real-time practical applications. Among these applications, the industrial benchmark of Tennessee Eastman Process (TEP) is widely used to illustrate and compare control and monitoring studies. However, due to the complexity of physical phenomena occurring in such process, no model-based approach for fault diagnosis has been developed and most of the diagnosis approaches applied to the TEP are based on experiences and qualitative reasoning that exploit the massive amount of available measurement data. In this paper, we propose to use the Bond Graph formalism as a multidisciplinary energetic approach that enables to obtain a graphical nonlinear model of the TEP not only for simulation purposes but also for monitoring tasks by generating formal fault indicators. In this study, the proposed BG model is validated from the experiment data and the problem of the TEP model design is hence overcome. A Bond Graph Modeling for Health Monitoring and Diagnosis of the Tennessee Eastman Process (PDF Download Available). Available from: https://www.researchgate.net/publication/314032904_A_Bond_Graph_Modeling... [accessed May 30, 2017]

A decision fusion based methodology for fault Prognostic and Health Management of complex systems

Prognostic and Health Management (PHM) represents an active field of research and a major scientific challenge in many domains. It usually focuses on the failure time or the Remaining Useful Life (RUL) prediction of a system. This paper presents a generic framework, based on a discrete Bayesian Network (BN), particularly tailored for decision fusion of heterogeneous prognostic methods. The BN parameters are computed according to the fixed prognostic objectives. The effectiveness of the proposed decision fusion based methodology for the prognostic is demonstrated through the RULs estimation of turbofan engines. The application highlights the ability of the approach to estimate RULs which overpasses the performance of most other published results in the literature

A New Multi-Objective Decision-Making Approach Applied to the Tennessee Eastman Process

In this paper, a generic framework and a new methodology aiming to decisions fusion of various Fault Detection and Diagnosis (FDD) methods are proposed. The framework consists of a discrete Bayesian Network (BN) and can handle all FDD methods, regardless of their a prior knowledge or requirements. The methodology expresses the FDD objectives to achieve the desired performance and results in a theoretical learning of the BN parameters. The development leads to a multi-objective problem under constraints, resolved with a lexicographic method.The e ectiveness of the proposed Multi-Objective Decision-Making (MODM) approach is validated through the Tennessee Eastman Process (TEP), as a challenging industrial benchmark problem. The application shows the signi cant improvement in FDD performances that can be ensured by the proposed methodology, in terms of high fault detection rate and small false alarm rate

Model-based approach for fault diagnosis using set-membership formulation

This paper describes a robust model-based fault diagnosis approach that enables to enhance the sensitivity analysis of the residuals. A residual is a fault indicator generated from an analytical redundancy relation which is derived from the structural and causal properties of the signed bond graph model. The proposed approach is implemented in two stages. The first stage consists in computing the residuals using available input and measurements while the second level leads to moving horizon residuals enclosures according to an interval consistency technique. These enclosures are determined by solving a constraint satisfaction problem which requires to know the derivatives of measured outputs as well as their boundaries. A numerical differentiator is then proposed to estimate these derivatives while providing their intervals. Finally, an inclusion test is performed in order to detect a fault upon occurrence. The proposed approach is well suited to deal with different kinds of faults and its performances are demonstrated through experimental data of an omni-directional robot

Robust fault detection in bond graph framework using interval analysis and Fourier-Motzkin elimination technique

This paper addresses the fault diagnosis problem of uncertain systems in the context of Bond Graph modelling technique. The main objective is to enhance the fault detection step based on Interval valued Analytical Redundancy Relations (named I-ARR) in order to overcome the problems related to false alarms, missed alarms and robustness issues. These I-ARRs are a set of fault indicators that generate the interval bounds called thresholds. A fault is detected once the nominal residuals (point valued part of I-ARRs) exceed the thresholds. However, the existing fault detection method is limited to parametric faults and it presents various limitations with regards to estimation of measurement signal derivatives, to which I-ARRs are sensitive. The novelties and scientific interest of the proposed methodology are: (1) to improve the accuracy of the measurements derivatives estimation by using a dedicated sliding mode differentiator proposed in this work, (2) to suitably integrate the Fourier-Motzkin Elimination (FME) technique within the I-ARRs based diagnosis so that measurements faults can be detected successfully. The latter provides interval bounds over the derivatives which are included in the thresholds. The proposed methodology is studied under various scenarios (parametric and measurement faults) via simulations over a mechatronic torsion bar system

Diagnostic à base de modèles et aide à la prise de décision robuste par une approche ensembliste

L\u27un des enjeux les plus importants des technologies impliquées dans l\u27ingénierie des systèmes complexes concerne aujourd\u27hui le diagnostic temps réel. Cette discipline repose principalement sur les algorithmes de détection et de localisation de défauts. Dans le présent papier, nous présentons une méthode générique permettant d\u27améliorer la robustesse de la procédure de détection de défauts. Cette méthode procède en deux étapes distinctes. Dans un premier temps, l\u27approche des Bond Graphs est utilisée pour générer, sur la base d\u27un modèle graphique, un ensemble d\u27indicateurs de défauts appelés résidus. Dans un second temps, les seuils de détectabilité permettant d\u27évaluer ces résidus sont déterminés grâce à l\u27analyse par intervalles et aux techniques de satisfaction de contraintes dans le but de réduire au maximum le taux de fausses alarmes et de non détection. Les performances de la méthode proposée sont démontrées par des données expérimentales provenant d\u27un robot omnidirectionnel