Diagnóstico baseado em modelos (Model Based Diagnosis - MBD) é uma técnica de Inteligência Artificial usada para encontrar componentes falhos em dispositivos físicos. MBD também tem sido utilizado para auxiliar programadores experientes a encontrarem falhas em seus programas, sendo essa técnica chamada de Depuração de Programas baseada em Modelos (Model Based Software Debugging - MBSD). Embora o MBSD possa auxiliar programadores experientes a entenderem e corrigirem suas falhas, essa abordagem precisa ser aprimorada para ser usada por aprendizes de programação. Esse trabalho propõe o uso da técnica de depuração hierárquica de programas, uma extensão da técnica MBSD, para que aprendizes de programação sejam capazes de depurar seus programas raciocinando sobre componentes abstratos, tais como: padrões elementares, funções e procedimentos. O depurador hierárquico de programas proposto foi integrado ao Dr. Java e avaliado com um grupo de alunos de uma disciplina de Introdução à Programação. Os resultados mostram que a maioria dos alunos foi capaz de compreender as hipóteses de falha geradas pelo depurador automático e usar essas informações para corrigirem seus programas. / Model Based Diagnosis (MBD) in Artificial Intelligence is a technique that has been used to detect faulty components in physical devices. MBD has also been used to help senior programmers to locate faults in software with a technique known as Model Based Software Debugging (MBSD). Although this approach can help experienced programmers to detect and correct faults in their programs, this approach must be improved to be used with novice programmers. This work proposes a hierarchical program diagnosis, a MBSD extension, to help novice programmers to debug programs by exploring the idea of abstract components, such as: elementary patterns, functions and procedures. The hierarchical program debugger proposed was integrated to the Dr. Java tool and evaluated with students of an introductory programming course. The results showed that most of the students were able to understand the hypotheses of failure presented by the automated debugger and use this information to provide a correction for their programs

<p>As space exploration vehicles travel deeper into space, their distance to earth increases.The increased communication delays and ground personnel costs motivatea migration of the vehicle health management into space. A way to achieve thisis to use a diagnosis system. A diagnosis system uses sensor readings to automaticallydetect faults and possibly locate the cause of it. The diagnosis system usedin this thesis is a model-based reasoning tool called RODON developed by UptimeSolutions AB. RODON uses information of both nominal and faulty behavior ofthe target system mathematically formulated in a model.The advanced diagnostics and prognostics testbed (ADAPT) developed at theNASA Ames Research Center provides a stepping stone between pure researchand deployment of diagnosis and prognosis systems in aerospace systems. Thehardware of the testbed is an electrical power system (EPS) that represents theEPS of a space exploration vehicle. ADAPT consists of a controlled and monitoredenvironment where faults can be injected into a system in a controlled manner andthe performance of the diagnosis system carefully monitored. The main goal of thethesis project was to build a model of the ADAPT EPS that was used to diagnosethe testbed and to generate decision trees (or trouble-shooting trees).The results from the diagnostic analysis were good and all injected faults thataffected the actual function of the EPS were detected. All sensor faults weredetected except faults in temperature sensors. A less detailed model would haveisolated the correct faulty component(s) in the experiments. However, the goal wasto create a detailed model that can detect more than the faults currently injectedinto ADAPT. The created model is stationary but a dynamic model would havebeen able to detect faults in temperature sensors.Based on the presented results, RODON is very well suited for stationary analysisof large systems with a mixture of continuous and discrete signals. It is possibleto get very good results using RODON but in turn it requires an equally goodmodel. A full analysis of the dynamic capabilities of RODON was never conductedin the thesis which is why no conclusions can be drawn for that case.</p><p> </p>

Diagnóstico baseado em modelos (Model Based Diagnosis - MBD) é uma técnica de Inteligência Artificial usada para encontrar componentes falhos em dispositivos físicos. MBD também tem sido utilizado para auxiliar programadores experientes a encontrarem falhas em seus programas, sendo essa técnica chamada de Depuração de Programas baseada em Modelos (Model Based Software Debugging - MBSD). Embora o MBSD possa auxiliar programadores experientes a entenderem e corrigirem suas falhas, essa abordagem precisa ser aprimorada para ser usada por aprendizes de programação. Esse trabalho propõe o uso da técnica de depuração hierárquica de programas, uma extensão da técnica MBSD, para que aprendizes de programação sejam capazes de depurar seus programas raciocinando sobre componentes abstratos, tais como: padrões elementares, funções e procedimentos. O depurador hierárquico de programas proposto foi integrado ao Dr. Java e avaliado com um grupo de alunos de uma disciplina de Introdução à Programação. Os resultados mostram que a maioria dos alunos foi capaz de compreender as hipóteses de falha geradas pelo depurador automático e usar essas informações para corrigirem seus programas. / Model Based Diagnosis (MBD) in Artificial Intelligence is a technique that has been used to detect faulty components in physical devices. MBD has also been used to help senior programmers to locate faults in software with a technique known as Model Based Software Debugging (MBSD). Although this approach can help experienced programmers to detect and correct faults in their programs, this approach must be improved to be used with novice programmers. This work proposes a hierarchical program diagnosis, a MBSD extension, to help novice programmers to debug programs by exploring the idea of abstract components, such as: elementary patterns, functions and procedures. The hierarchical program debugger proposed was integrated to the Dr. Java tool and evaluated with students of an introductory programming course. The results showed that most of the students were able to understand the hypotheses of failure presented by the automated debugger and use this information to provide a correction for their programs

Rodon is a diagnostic tool developed by Sörman. SAAB’s interest in Rodon regards the possibility to use the tool for development and operations of aircraft systems. The main goal of this thesis was to evaluate the capacity of Rodon and determine how SAAB can use the diagnostic tool during development and operations. The tool uses model based diagnosis with artificial intelligence for fault isolation which is a powerful approach. If Rodon is introduced at SAAB, then detailed models of systems will be necessary to create, including the nominal behavior of the system and different faulty behaviors. In order to achieve high quality fault isolation, it is necessary to have complete and consistent models. To be able to use all applications that Rodon feature for a modeled system, preferable characteristics are that the model should be static, have discrete control signals, and have well defined system behavioral modes. During development of a system Rodon can be used to improve and easy the work for failure analysis, guidance of sensor placements, evaluation of tests, generation of decision structures, and fault isolation. Since design of tests during development is a desirable application that Rodon does not have, two different methods are presented that utilizes Rodon to generate all possible limit checking tests. In conclusion, Rodon can be very useful in several different aspects if introduced, but benefits gained by using Rodon will have to be compared to the labor cost of creating good models.

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<p>We investigate how the knowledge in knowledge technology applications for knowledge management can be represented to let the user directly manage the knowledge content of the applications.</p><p>In paper I we design a representation of diagnosis knowledge that allows the user to add new components and inspect the function of the device. The representation allows an integration of model based knowledge with compiled and heuristic knowledge so that the device and its function can be represented a suitable level of abstraction and let other parts be represented as non-model based knowledge.</p><p>In paper II we use simplified rules for describing the time, resources, activities and amounts required in a tunnelling project and a simulation engine for estimating time and amounts consumed in the tunnelling process. The rules are designed to allow a user to change the facts and computations of the system.</p><p>In paper III we present the constraint modelling language CML and show how to model a nurse scheduling problem and a train scheduling problem without programming. The idea is to preserve the problem structure of the domain, allowing constraint formulations that reflect natural language expressions familiar to the users. CML problem specifications are transformed automatically to standard constraint programs </p><p>In paper IV we investigate the use of decision tables for representing requirements on staff scheduling explicitly, providing structure, decision support and overview to the user. The requirements are compiled automatically to a program that use hand-written procedures for efficient scheduling.</p><p>It seems possible to let the user modify central parts of the knowledge content in the applications with these representations, by using various illustration techniques. The techniques used are object-based graphics for manipulating device components and connections in diagnosis, simplified rules for simulation of tunnelling activities, text-based query language specification of scheduling problems and finally, decision tables for constraint problems and decision support.</p>

We investigate how the knowledge in knowledge technology applications for knowledge management can be represented to let the user directly manage the knowledge content of the applications. In paper I we design a representation of diagnosis knowledge that allows the user to add new components and inspect the function of the device. The representation allows an integration of model based knowledge with compiled and heuristic knowledge so that the device and its function can be represented a suitable level of abstraction and let other parts be represented as non-model based knowledge. In paper II we use simplified rules for describing the time, resources, activities and amounts required in a tunnelling project and a simulation engine for estimating time and amounts consumed in the tunnelling process. The rules are designed to allow a user to change the facts and computations of the system. In paper III we present the constraint modelling language CML and show how to model a nurse scheduling problem and a train scheduling problem without programming. The idea is to preserve the problem structure of the domain, allowing constraint formulations that reflect natural language expressions familiar to the users. CML problem specifications are transformed automatically to standard constraint programs In paper IV we investigate the use of decision tables for representing requirements on staff scheduling explicitly, providing structure, decision support and overview to the user. The requirements are compiled automatically to a program that use hand-written procedures for efficient scheduling. It seems possible to let the user modify central parts of the knowledge content in the applications with these representations, by using various illustration techniques. The techniques used are object-based graphics for manipulating device components and connections in diagnosis, simplified rules for simulation of tunnelling activities, text-based query language specification of scheduling problems and finally, decision tables for constraint problems and decision support.

As space exploration vehicles travel deeper into space, their distance to earth increases.The increased communication delays and ground personnel costs motivatea migration of the vehicle health management into space. A way to achieve thisis to use a diagnosis system. A diagnosis system uses sensor readings to automaticallydetect faults and possibly locate the cause of it. The diagnosis system usedin this thesis is a model-based reasoning tool called RODON developed by UptimeSolutions AB. RODON uses information of both nominal and faulty behavior ofthe target system mathematically formulated in a model.The advanced diagnostics and prognostics testbed (ADAPT) developed at theNASA Ames Research Center provides a stepping stone between pure researchand deployment of diagnosis and prognosis systems in aerospace systems. Thehardware of the testbed is an electrical power system (EPS) that represents theEPS of a space exploration vehicle. ADAPT consists of a controlled and monitoredenvironment where faults can be injected into a system in a controlled manner andthe performance of the diagnosis system carefully monitored. The main goal of thethesis project was to build a model of the ADAPT EPS that was used to diagnosethe testbed and to generate decision trees (or trouble-shooting trees).The results from the diagnostic analysis were good and all injected faults thataffected the actual function of the EPS were detected. All sensor faults weredetected except faults in temperature sensors. A less detailed model would haveisolated the correct faulty component(s) in the experiments. However, the goal wasto create a detailed model that can detect more than the faults currently injectedinto ADAPT. The created model is stationary but a dynamic model would havebeen able to detect faults in temperature sensors.Based on the presented results, RODON is very well suited for stationary analysisof large systems with a mixture of continuous and discrete signals. It is possibleto get very good results using RODON but in turn it requires an equally goodmodel. A full analysis of the dynamic capabilities of RODON was never conductedin the thesis which is why no conclusions can be drawn for that case.

Cette thèse concerne le diagnostic de processus dynamiques basée sur la Théorie des Observations Datées, une théorie mathématique conçue pour la modélisation et le raisonnement à partir de données datées. Les contributions présentées dans ce mémoire sont 1) une extension de la méthodologie d'ingénierie des connaissances TOM4D (Timed Observation Modeling for Diagnosis) aux réseaux de processus dynamiques, 2) l'algorithme temps réel et any-time TOM4E (Timed Observation Management for Explanation) qui utilise les modèles TOM4D pour diagnostiquer les comportements dans un réseau de processus dynamiques à partir de données datées et 3) l'application de TOM4D et TOM4E au diagnostic du barrage hydraulique des Sapins (France), un problème particulièrement difficile. TOM4D est une approche de diagnostic à partir de multiples modèles dirigée par la syntaxe ou l'introduction de la sémantique est contrôlée par la Combinaison de l'approche conceptuelle de CommonKADS au tétraèdre des états de la physique newtonienne. Les fonctions Detect, Describe et Explain de TOM4E utilisent les modèles d'observation déduit des modèles de comportement de TOM4D pour identifier les comportements potentiels des processus. Pour des raisons de simplicité, la présentation de TOM4D et de TOM4E est effectuée à l'aide d'un exemple didactique tirée de la littérature spécialisée dans le domaine du diagnostic. L'application au diagnostic du barrage des Sapins démontre l'intérêt de l'approche : leur usage aurait permis d'identifier le premier problème huit ans avant sa quasi-destruction, la présence d'eau étant mise en évidence sept ans avant. / This thesis proposes a diagnosis approach of dynamic process based on the Timed Observation Theory, a mathematical framework for modeling and reasoning about dynamic process from timed data. The contributions of this works are i) an extension of the TOM4D (Timed Observation Modeling for Diagnosis) Knowledge Engineering methodology to networks of dynamic processes, ii) a real-time and any-time diagnosis algorithm called TOM4E (Timed Observation Management for Explanation) that uses the TOM4D models to diagnose behaviors in a network of dynamic processes and iii) the application of TOM4D and TOM4E to the diagnosis of the French Sapin's hydraulic dam, a particularly difficult real-world diagnosis problem. TOM4D is a is a primarily syntax-driven approach of Multi-Model Based Diagnosis where semantic content is introduced in a gradual and controlled way through the combination of the CommonKADS conceptual approach and the Tetrahedron of States of Newton's physical laws.TOM4E algorithm is based on the Detect, Describe and Explain functions which uses observation models translated from the TOM4D behavioral models. For simplicity reasons, the presentation of TOM4D and TOM4E is made with a unique didactic example provided from the literature of the diagnosis domain. The example of Sapin's dam makes the demonstration of the interest of the proposed approach: using them, the first Sapin's dam problem would have been identified eight years before its quasi-failure, and the presence of water being highlighted seven years before.

La surveillance et l'amélioration des modes de fonctionnement des systèmes propulsifs des lanceurs représentent des défis majeurs de l'industrie aérospatiale. En effet, une défaillance ou un dysfonctionnement du système propulsif peut avoir un impact significatif pour les clients institutionnels ou privés et entraîner des catastrophes environnementales ou humaines. Des systèmes de gestion de la santé (HMS) pour les moteurs fusée à ergols liquides (LPREs), ont été mis au point pour tenir compte des défis actuels en abordant les questions de sureté et de fiabilité. Leur objectif initial est de détecter les pannes ou dysfonctionnements, de les localiser et de prendre une décision à l’aide de Redlines et de systèmes experts. Cependant, ces méthodes peuvent induire de fausses alarmes ou des non-détections de pannes pouvant être critiques pour la sécurité et la fiabilité des opérations. Ainsi, les travaux actuels visent à éliminer certaines pannes critiques, mais aussi diminuer les arrêts intempestifs. Les données disponibles étant limitées, des méthodes à base de modèles sont essentiellement utilisées. La première tâche consiste à détecter les défaillances de composants et/ou d'instruments à l'aide de méthodes de détection et de localisation de fautes (FDI). Si la faute est considérée comme mineure, des actions de « non-arrêt » sont définies pour maintenir les performances de l'ensemble du système à un niveau proche de celles souhaitées et préserver les conditions de stabilité. Il est donc nécessaire d’effectuer une reconfiguration robuste (incertitudes, perturbations inconnues) du moteur. Les saturations en entrée doivent également être prises en compte dans la conception de la loi de commande, les signaux de commande étant limités en raison des caractéristiques ou performances des actionneurs physiques. Les trois objectifs de cette thèse sont donc : la modélisation des différents sous-systèmes principaux d’un LPRE, le développement d’algorithmes de FDI sur la base des modèles établis et la définition d’un système de reconfiguration du moteur en temps réel pour compenser certains types de pannes. Le système de FDI et Reconfiguration (FDIR) développé sur la base de ces trois objectifs a ensuite été validé à l’aide de simulations avec CARINS (CNES) et du banc d’essai MASCOTTE (CNES/ONERA). / Monitoring and improving the operating modes of launcher propulsion systems are major challenges in the aerospace industry. A failure or malfunction of the propulsion system can have a significant impact for institutional or private customers and results in environmental or human catastrophes. Health Management Systems (HMS) for liquid propellant rocket engines (LPREs), have been developed to take into account the current challenges by addressing safety and reliability issues. Their objective was initially to detect failures or malfunctions, isolate them and take a decision using Redlines and Expert Systems. However, those methods can induce false alarms or undetected failures that can be critical for the operation safety and reliability. Hence, current works aim at eliminating some catastrophic failures but also to mitigate benign shutdowns to non-shutdown actions. Since databases are not always sufficient to use efficiently data-based analysis methods, model-based methods are essentially used. The first task is to detect component and / or instrument failures with Fault Detection and Isolation (FDI) approaches. If the failure is minor, non-shutdown actions must be defined to maintain the overall system current performances close to the desirable ones and preserve stability conditions. For this reason, it is required to perform a robust (uncertainties, unknown disturbances) reconfiguration of the engine. Input saturation should also be considered in the control law design since unlimited control signals are not available due to physical actuators characteristics or performances. The three objectives of this thesis are therefore: the modeling of the different main subsystems of a LPRE, the development of FDI algorithms from the previously developed models and the definition of a real-time engine reconfiguration system to compensate for certain types of failures. The developed FDI and Reconfiguration (FDIR) scheme based on those three objectives has then been validated with the help of simulations with CARINS (CNES) and the MASCOTTE test bench (CNES/ONERA).