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 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

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

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

Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges

Fault Diagnosis and Health Monitoring (FD-HM) for modern control systems have been an active area of research over the last few years. Model-based FD-HM computational approaches have been extensively developed to detect and locate faults by considering logical or mathematical description of the monitored process. However, because of parametric, measurement and model uncertainties, applicable approaches that endeavor to locate faults with great accuracy are likely to give false alarms. Recently, many research works have been conducted in order to tackle this issue by making a tradeoff between accuracy and robustness during the fault detection phase. Due to the recent advances in sensor technology, computational capabilities and dedicated software/hardware interfaces, data-driven FD-HM approaches have demonstrated that highly accurate fault detection is possible when the system monitoring data for nominal and degraded conditions are available. Therefore, it seems that more than one approach is usually required for developing a complete robust fault detection and diagnosis tool. In this paper, the features of different model-based and data-driven approaches are investigated separately as well as the existing works that attempted to integrate both of them. In this latter context, there have been only few works published in the literature and hence reviewing and discussing them is strongly motivated by providing a good reference for those interested in developing hybrid approaches for FD-HM

Generic Framework for Hybrid Fault Diagnosis and Health Monitoring of the Tennessee Eastman Process

International audienc

Fusion de modèles quantitatifs et qualitatifs pour le développement d’une approche intégrée dédiée à la surveillance des systèmes dynamiques

National audienc

A New Hybrid Approach for Fault Detection and Diagnosis

Fault detection and isolation based on hybrid approaches have been an active eld of research over the last few years. From a practical point of view, the development of generic and uni ed approaches for industrial supervision systems design is a key challenge. The main methodological contribution of the present work is to develop a hybrid approach properly tailored for such challenge. The proposed approach uses the Bond Graph formalism to systematically develop computational models and algorithms for robust fault detection and isolation. The resulting outcomes are extended to a proposed data-driven approach which consists of transforming historical process data into a meaningful alphabetical model incorporated within a Bayesian network. This new hybrid methodology bene ts from all the knowledge available on the system and provides a more comprehensive solution in order to increase the overall con dence in the diagnosis and the performances. The e ectiveness of the developed hybrid approach is validated by the well-known Tennessee Eastman Benchmark process