Hybrid Automaton Incremental Construction for Online Diagnosis

This paper proposes a method to track the system mode and diagnose a hybrid system without building an entire diagnoser off-line. The method is supported by a hybrid automaton model that represents the hybrid system continuous and discrete behavioral dynamics. Diagnosis is performed by interpreting the events and measurements issued by the physical system directly on the hybrid automaton model. This interpretation leads to building the useful parts of the diagnoser incrementally, developing only the branches that are required to explain the occurrence of the incoming events. The resulting diagnoser adapts to the system operational life and is much less demanding in terms of memory storage. The proposed framework subsumes previous works in that it copes with both structural and non-structural faults. The method is validated on an application case study based on the sewer network of the Barcelona city.Peer ReviewedPostprint (author’s final draft

Contributions of diagnosis reasoning to the general demand for AI in the industry

International audienceAI applications have never been as popular as today. The enthusiasm of all branches of the industry is in tune and agrees to say that AI technologies can lift many industrial locks for customer value creation, productivity improvement, and insight discovery. Huge business opportunities are expected in this process. Numerous applications are also foreseen in medicine and heath care, agriculture and environment, transport/mobility, and energy domains. Faced with this expectation, where do we situate ourselves ? In this talk, I will focus on engineering and process applications and will identify the main requests and the needs in these domains. I will then focus on my area of expertise, which is diagnostic reasoning, and explain how existing diagnosis theories can bring their contribution based on the presentation of some applications that address specific needs in these domains. I will conclude my talk by drawing my picture of what is still missing to satisfy the current expectation

Bridging Control and Artificial Intelligence Theories for Diagnosis: A survey

International audienceDiagnosis is the process of identifying or determining the nature and root cause of a failure, problem, or disease from the symptoms resulting from selected measurements, checks or tests. The different facets of this problem and the wide spectrum of classes of systems make it interesting to several communities and require bridging several theories. Diagnosis is actually a functional fragment in fault management architectures and it must smoothly interact with other functions. This paper presents diagnosis as it is understood in the Control and Artificial Intelligence fields, and exemplifies how different theories of these fields can be synergistically integrated to provide better diagnostic solutions and to achieve improved fault management in different environments

Contributions of diagnostic to the general demand for AI in the industry

International audienceAI applications have never been as popular as today. The enthusiasm of all branches of the industry is in tune and agrees to say that AI technologies can lift many industrial locks for customer value creation, productivity improvement, and insight discovery. Huge business opportunities are expected in this process. Numerous applications are also foreseen in medicine and heath care, agriculture and environment, transport/mobility, and energy domains. Faced with this expectation, where does the diagnosis community situate itself ? In this talk, I will focus on engineering and process applications and will identify some requests and needs in these domains. I will then focus on diagnosis reasoning and explain how existing diagnosis theories can bring their contribution based on the presentation of some applications that address specific needs in these domains. I will conclude my talk by drawing my picture of what is still missing to satisfy the current expectations

An Algorithm for Active Diagnosis of Hybrid Systems Casted in the DES Framework

6 pages.International audienceOn-line diagnosis must accommodate the existing sensoring capabilities of a system, which often results in limited diagnosability. However, although faults may not be always discriminable, there are generally operating modes of the system in which they are. Active diagnosis relies on applying specific inputs to the system so as to exhibit additional symptoms that help refining the diagnosis. The idea of this paper is to use the diagnosability properties to drive the system towards modes with increased diagnosability with respect of safety considerations. A new finite state machine called the active diagnoser is defined by abstracting continuous dynamics and taking into account controllability and safety constraints. The active diagnosis problem is then formulated as a conditional planning problem. Hence, the active diagnoser is transformed in an AND-OR graph and active diagnosis plans are computed by an appropriate graph exploration algorithm

Rapport de synthèse n°6 - Projet COCOTIER: COncept de COckpit et Technologies Intégrées En Rupture (Convention de soutien n°2019-08)

Ce rapport de synthèse présente les activités réalisées ainsi que les résultats obtenus par le CNRS LAAS sur le flux 5 du projet COCOTIER « Nouveaux capteurs et algorithmes de fusion de données pour amélioration des fonctions de Navigation et Surveillance ». Ce projet a impliqué deux équipes de recherche du LAAS :-L’équipe RAP (Robotique, Action et Perception – M.Devy) a participé au sous-flux 5.1 « Etude et Modélisation des Capteurs », dans les tâches 5.1 (resp. OKTAL SE) et 5.5 (resp. AIRBUS) sur la thématique Perception.-L’équipe DISCO (Diagnostic, Supervision et Contrôle – L.Travé Massuyés) a participé au sous-flux 5.2 « Algorithmes de fusion de données et nouvelles architectures multi-capteurs », dans la tâche 5.9 (resp. AIRBUS) sur la thématique Diagnostic

The order of magnitude models as qualitative algebras

International audienceThis paper provides a unifying mathematical framework for orders of magnitude models used in Qualitative Physics. An axiomatic of the qualitative equality is provided and a general algebraic structure called qualitative algebra is defined. It is shown that the usual model (+,-,0,?) and the extended model recently introduced by Dubois and Prade are particular cases in the class of models that are generated from a partition of the real line. Any of these models can be structured as qualitative algebra. On the other hand, we characterize those qualitative algebras that are isomorphous, in a qua litative sense. Besides, it is shown that all these models can be embedded into one another as qualitative subalgebras

Rapport de synthèse n°6 - Projet COCOTIER: COncept de COckpit et Technologies Intégrées En Rupture (Convention de soutien n°2019-08)

Ce rapport de synthèse présente les activités réalisées ainsi que les résultats obtenus par le CNRS LAAS sur le flux 5 du projet COCOTIER « Nouveaux capteurs et algorithmes de fusion de données pour amélioration des fonctions de Navigation et Surveillance ». Ce projet a impliqué deux équipes de recherche du LAAS :-L’équipe RAP (Robotique, Action et Perception – M.Devy) a participé au sous-flux 5.1 « Etude et Modélisation des Capteurs », dans les tâches 5.1 (resp. OKTAL SE) et 5.5 (resp. AIRBUS) sur la thématique Perception.-L’équipe DISCO (Diagnostic, Supervision et Contrôle – L.Travé Massuyés) a participé au sous-flux 5.2 « Algorithmes de fusion de données et nouvelles architectures multi-capteurs », dans la tâche 5.9 (resp. AIRBUS) sur la thématique Diagnostic

Le diagnostic automatique, c’est quoi ?

National audienceDiagnostiquer est une activité intellectuelle que l'on retrouve dans de nombreux secteurs d'activité. Le médecin diagnostique l'état de santé de son patient à partir des symptômes présents afin de déterminer la maladie. Le garagiste analyse l'état des voitures afin de trouver la panne. L'opérateur de maintenance aéronautique analyse les données de l'avion pour remplacer les composants défaillants. Le diagnosticien de bâtiment recherche les fuites énergétiques (perte de chaleur) pour proposer des solutions permettant d'y remédier. Toutes ces activités reposent sur un même type de raisonnement et l'intelligence artificielle apporte des moyens pour automatiser ce raisonnement et assister la société dans ses différentes activités socio-économiques

Gas-turbine condition monitoring using qualitative model-based diagnosis

International audienceGas turbines are critical to the operation of most industrial plants, and their associated maintenance costs can be extremely high. To reduce those costs and increase the availability of their gas turbines, plant operators have for many years relied on routine preventative maintenance-routinely checking and solving small problems before they grow into major ones. Recently, however, the power industry has moved sharply toward condition-based maintenance and monitoring. In this approach, intelligent computerized systems monitor gas turbines to establish maintenance needs based on the turbine's condition rather than on a fixed number of operating hours. By integrating several AI technologies - including qualitative model-based reasoning - the Tiger system significantly cuts costs and improves performance by using control-system information to perform condition monitoring for gas-turbine engines