Toward a decompositional and incremental approach to diagnosis of dynamic systems from timed observations

International audienceIt is now well-known that the size of the model is the bottleneck when using model-based approaches to diagnose complex systems. To answer this problem, decompositional and multi modelling approaches have been proposed. In this paper, we propose a multi-modelling method called TOM4D (Timed Observations Modelling for Diagnosis) able to cope with dynamic aspects. It relies on four models: perception, structural, functional and behaviour models. The behaviour model is described through system component models as a set of component behaviour models and the global diagnosis is computed from the component diagnoses (also called local diagnoses). Another problem, which is far less considered, is the size of the diagnosis itself. However, it can also be huge enough, especially when dealing with dynamic system. To solve this problem, we propose in this paper to use The Timed Observation Theory. In this context, we characterize the diagnosis using TOM4D and the timed observation theory. We show their relevance to get a tractable representation of diagnosis. To illustrate the impact on the diagnosis size, experimental results on a hydraulic example are given

Process diagnosis with timed observation

International audienceIn this paper we propose the use of the Timed Observation theory as a powerful frameworks for model-based diagnosis. In fact, they provide a global formalism for modelling a dynamic system (TOM4D), for characterizing and computing diagnoses of the system under investigation

Modelling and diagnosis of dynamic systems from timed observations

International audienceThis paper proposes the use of the Timed Observation theory as a powerful framework for model-based diagnosis. In fact, this theory provides a global formalism for modelling a dynamic system (TOM4D), for characterizing and computing diagnoses of the system under investigation

Diagnostic du comportement des barrages basé sur une approche multi-modèles

International audienceDams are heterogeneous structures featured by complex behaviours that evolve through time due to natural ageing. As a consequence, it is essential to develop modelling approaches for diagnosis taking into account the temporal aspect to comply the assessment of reliability and safety at the current time, to diagnose the causes of reliability and safety deterioration in the past and to forecast dam reliability and safety at different time scales. In this paper, we propose a multi-modelling method called TOM4D (Timed Observations Modelling for Diagnosis) able to cope with dynamic aspects. It relies on four models: perception, structural, functional and behavioural models. We use this methodology to assess the dam behaviour at different times and the detection of behaviour deterioration. The resulting model allows to characterize and to compute diagnosis of the system under investigation. The diagnosis was applied at two levels, the component level (local diagnosis) and the dam level (global diagnosis). Only a few process variables are used to model the dam behaviour and its components. These process variables are linked to real, visual and monitoring measurements carried out on the dam (piezometry, outflows, detection of leakage...). An application on a real case is performed.Les barrages sont caractérisés par des comportements complexes qui évoluent au cours du temps du fait d’un vieillissement naturel. Il est par conséquent pertinent de développer des approches de modélisation prenant en compte les aspects temporels. Le but de cette modélisation est de pouvoir ensuite conduire les tâches d’évaluation d’un barrage à un instant donné, de diagnostic des causes de la détérioration de sa sécurité dans le passé et d’analyser la fiabilité et la sécurité du barrage à différentes échelles de temps. Dans cet article, nous proposons une méthode de modélisation basée sur une approche multi-modèles appelée TOM4D (Timed Observations Modeling for Diagnosis) pour le diagnostic du comportement dynamique des barrages. Quatre modèles sont décrits : un modèle de perception, un modèle structurel, un modèle comportemental et un modèle fonctionnel. Nous utilisons ces modèles pour analyser le comportement du barrage à différents instants et en détecter l’éventuelle détérioration. Les modèles résultants de l’étape de modélisation, permet de caractériser et de calculer les diagnostics du barrage. Pour conduire les tâches d’évaluation, diagnostic, deux niveaux sont considérés : les composants individuels (diagnostic local) et le barrage dans sa globalité (diagnostic global). Une application sur un cas réel est effectuée

Évaluation de la sécurité des barrages hydrauliques basée sur la théorie des observations datées

International audienceThe safety control process of industrial systems (considered to be dynamic systems) needs to take in account physical processes (e.g. building ageing), informational processes (data collection and processing), decisional processes (data aggregation), and has to consider various constraints (e.g. economic and regulatory). The improvement of informational and decisional processes with the aim of controlling physical processes is based on the development of models and algorithms for measurement, assessment, control, diagnosis and prognostic. In the domain of dam management, assessment of reliability and safety, fault diagnosis, and corrective action proposals are carried out by expert engineers during dam reviews. With the perspective to assist these expert engineers, it is of great importance to develop methods and tools to manage the dynamic behaviour of dams and to model the processes at the same level of abstraction that is used by experts. In this chapter, the authors tackle the cognitive process of the diagnosis by means of a formal multi-modelling method and a diagnosis algorithm. The multi-modelling method called Timed Observations Method for Diagnosis (TOM4D) is based on the elaboration of four models: a Structural Model describing the relations between the components of the system, a Functional Model providing the relations between the values of the process variables (i.e. a set of mathematical functions), a Behavioural Model defining the states of the process and the discrete events firing the state transitions, and a Perception Model composed of a set of abstract variables, a set of thresholds associated to these variables and a set of constraints. The resulting process allows the automatic fault detection, identification and diagnosis and it is applied to hydraulic dam safety

Drainage fracture networks in elastic solids with internal fluid generation

Experiments in which CO2 gas was generated by the yeast fermentation of sugar in an elastic layer of gelatine gel confined between two glass plates are described and analyzed theoretically. The CO2 gas pressure causes the gel layer to fracture. The gas produced is drained on short length scales by diffusion and on long length scales by flow in a fracture network, which has topological properties that are intermediate between river networks and hierarchical-fracture networks. A simple model for the experimental system with two parameters that characterize the disorder and the intermediate (river-fracture) topology of the network was developed and the results of the model were compared with the experimental results

Data underpinning High-fidelity phase and amplitude control of phase-only computer generated holograms using conjugate gradient minimisation

Data underpinning: D. Bowman, T. L. Harte, V. Chardonnet, C. De Groot, S. J. Denny, G. Le Goc, M. Anderson, P. Ireland, D. Cassettari, & G. D. Bruce, "High-fidelity phase and amplitude control of phase-only computer generated holograms using conjugate gradient minimisation" Opt. Express 25, 11692-11700 (2017) https://www.osapublishing.org/oe/abstract.cfm?uri=oe-25-10-11692 The algorithm used in the work may be found on github, see link. Codes may be used freely, but please cite the above article