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

Validation of a Novel Sensing Approach for Continuous Pavement Monitoring Using Full-Scale APT Testing

The objective of this paper is to present a novel approach for the continuous monitoring of pavement condition through the use of combined piezoelectric sensing and novel condition-based interpretation methods. The performance of the developed approach is validated for the detection of bottom-up fatigue cracking through full-scale accelerated pavement testing (APT). The innovative piezoelectric sensors are installed at the bottom of a thin 102 mm (4 in.) asphalt layer. The structure is then loaded until failure (up to 1 million loading cycles in this study). The condition-based approach, used in this work, does not rely on stain measurements and allows users to bypass the need for any structural or finite-element models. Instead, the data compression approach relies on variations in strain energy harvested by smart sensors to track changes in material and structural conditions. Falling weight deflectometer (FWD) measurements and visual inspections were used to validate the observations from the sensing system. The results in this paper present a first large-scale validation in pavement structures for a piezopowered sensing system combined with a new response-only based approach for data reduction and interpretation. The proposed data analysis method has demonstrated a very early detection capability compared to classical inspection methods, which unveils a huge potential for improved pavement monitoring

Phylogeography of the marbled crab Pachygrapsus marmoratus (Decapoda, Grapsidae) along part of the African Mediterranean coast reveals genetic homogeneity across the Siculo-Tunisian Strait versus heterogeneity across the Gibraltar Strait

We investigate the influence of previously postulated biogeographic barriers in the Mediterranean Sea on the population genetic structure of a highly dispersive and continuously distributed coastal species. In particular, we examine nuclear and mitochondrial genetic variation in the marbled crab, Pachygrapsus marmoratus, across part of the African Mediterranean coast in order to assess the influence of the Siculo-Tunisian Strait on its population genetic structure. Four polymorphic microsatellite loci were genotyped for 110 individuals, collected from eight locations covering parts of the Algerian, Tunisian and Libyan coasts. In addition, mtDNA corresponding to the Cox1 gene was sequenced for 80 samples. The corresponding results show contrasting patterns of genetic differentiation. While mtDNA results revealed a homogeneous haplotype composition in our study area, microsatellite data depicted genetic differentiation among populations, but not associated with any geographic barrier. This pattern, already recorded for this species from different geographic regions, may hint at the involvement of a complex series of abiotic and biotic factors in determining genetic structure. Demographic history reconstruction, inferred from mtDNA data, supports demographic and spatial expansion for the North African metapopulation dating back to the Mid-Pleistocene and following an historical bottleneck. Comparison of these African mitochondrial sequences with new sequences from a Turkish population and previously published sequences revealed a weak but significant separation of Atlantic and Mediterranean populations across the Gibraltar Strait, which was not recorded in previous studies of this grapsid species

Data compression approach for long-term monitoring of pavement structures

Pavement structures are designed to withstand continuous damage during their design life. Damage starts as soon as the pavement is open to traffic and increases with time. If maintenance activities are not considered in the initial design or considered but not applied during the service life, damage will grow to a point where rehabilitation may be the only and most expensive option left. In order to monitor the evolution of damage and its severity in pavement structures, a novel data compression approach based on cumulative measurements from a piezoelectric sensor is presented in this paper. Specifically, the piezoelectric sensor uses a thin film of polyvinylidene fluoride to sense the energy produced by the micro deformation generated due to the application of traffic loads. Epoxy solution has been used to encapsulate the membrane providing hardness and flexibility to withstand the high-loads and the high-temperatures during construction of the asphalt layer. The piezoelectric sensors have been exposed to three months of loading (approximately 1.0 million loads of 65 kN) at the French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR) fatigue carrousel. Notably, the sensors survived the construction and testing. Reference measurements were made with a commercial conventional strain gauge specifically designed for measurements in hot mix asphalt layers. Results from the carrousel successfully demonstrate that the novel approach can be considered as a good indicator of damage progression, thus alleviating the need to measure strains in pavement for the purpose of damage tracking

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

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