<p>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.</p><p>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.</p><p>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.</p><p>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.</p>

Fault isolation is the process of reasoning required to find the cause of a system failure. In a model-based approach, the available information is a model of the system and some observations. Using knowledge of how the system generally behaves, as given in the system model, together with partial observations of the events of the current situation the task is to deduce the failure causing event(s). In our setting, the observable events manifest themselves in a message log. We study post mortem fault isolation for moderately concurrent discrete event systems where the temporal order of logged messages contains little information. To carry out fault isolation one has to study the correlation between observed events and fault events of the system. In general, such study calls for exploration of the state space of the system, which is exponential in the number of system components. Since we are studying a restricted class of all possible systems we may apply aggressive specialized abstraction policies in order to allow fault isolation without ever considering the often intractably large state space of the system. In this thesis we describe a mathematical framework as well as a prototype implementation and an experimental evaluation of such abstraction techniques. The method is efficient enough to allow for not only post mortem fault isolation but also design time diagnosability analysis of the system, which can be seen as a non-trivial way of analyzing all possible observations of the system versus the corresponding fault isolation outcome. This work has been supported by VINNOVA’s Competence Center ISIS.

Traditional diagnosis has been performed with hardware redundancy and limit checking. The development of more powerful computers have made a new kind of diagnosis possible. Todays computing power allows models of the system to be run in real time and thus making model-based diagnosis possible. The objective with this thesis is to investigate the potential of model-based diagnosis, especially when combined with active diagnosis. The diagnosis system has been applied on a model of the JAS39 Gripen fuel pressurization system. With the sensors available today no satisfying diagnosis system can be built, however, by adding a couple of sensors and using active model-based diagnosis all faults can be detected and isolated into a group of at most three components. Since the diagnosis system in this thesis only had a model of the real system to be tested at, this thesis is not directly applicable on the real system. What can be used is the diagnosis approach and the residuals and decision structure developed here.

To simplify troubleshooting and reliability of a process, a diagnosis system can supervise the process and alarm if any faults are detected. A diagnosis system can also identify one, or several faults, i.e. isolate faults, that may have caused the alarm. If model-based diagnosis is used, tests based on observations from the process are compared to a model of the process to diagnose the process. It can be a hard task to find which tests to be used for maximal fault detection and fault isolation. Structural Methods require not very detailed knowledge of the process to be diagnosed and can be used to find such tests early in the design of new processes. Sensors are used to get observations of a process. Therefore, sensors placed on different positions in the process gives different possibilities for observations. A specific set of sensors are in this work called a sensor configuration. This thesis contributes with a method to predict and examine the fault detection and fault isolation possibility. By using these two diagnosis properties, a suitable sensor configuration is computed and tests to be used in a future diagnosis system are suggested. For this task an algorithm which can be used in the design phase of diagnosis systems, and a Matlab implementation of this algorithm are described. In one part of this work the Matlab implementation and the algorithm are used to study how a model-based diagnosis-system can be used to supervise the fuel system in an Unmanned Aerial Vehicle (UAV).

Today’s society depends on complex and technically advanced mechanical systems, often containing a variety of different components. Despite careful development andconstruction, some of these components may eventually fail. To avoid unnecessary damage, for example environmental or financial, there is a need to locate and diagnose these faults as fast as possible. This can be done with a diagnostic system, which should produce an alarm if there is a fault in the mechanical system and, if possible, indicate the reason behind it. In model based diagnosis, a mathematical model of a fault free system is used to detect if the monitored system contain any faults. This is done by constructing fault indicators, called fault tests, consisting of equations from different parts of the model. Finding these parts is a time-consuming and demanding task, hence it is preferable if as much as possible of this process can be automated. In this thesis an algorithm that finds all parts of a system that can be used to create these fault tests is presented. To make this analysis feasible, in industrial applications, a simplified version of a system model called a structural model is used. Since the models considered in this thesis are implemented in the mathematical software Simulink, a method for transforming Simulink models into analytical equations and structural models is described. As a way of increasing the diagnostic performance for a model based diagnostic system, information about different faults, called fault models, can be included in the model. However, since the models in this thesis are implemented in Simulink, there is no direct way in which this can be preformed. This thesis describes a solution to this problem. The correctness of the algorithms in this thesis are proved and they have been applied, with supreme results, to aScania truck engine model.

<p>Traditional diagnosis has been performed with hardware redundancy and limit checking. The development of more powerful computers have made a new kind of diagnosis possible. Todays computing power allows models of the system to be run in real time and thus making model-based diagnosis possible. </p><p>The objective with this thesis is to investigate the potential of model-based diagnosis, especially when combined with active diagnosis. The diagnosis system has been applied on a model of the JAS39 Gripen fuel pressurization system. </p><p>With the sensors available today no satisfying diagnosis system can be built, however, by adding a couple of sensors and using active model-based diagnosis all faults can be detected and isolated into a group of at most three components. </p><p>Since the diagnosis system in this thesis only had a model of the real system to be tested at, this thesis is not directly applicable on the real system. What can be used is the diagnosis approach and the residuals and decision structure developed here.</p>

Defects in computer programs have great social and economic impacts and should be eliminated as much as possible. Since testing and debugging are among the most costly and time consuming tasks in the software development life cycle, a variety of intelligent debugging aids have been proposed within the last three decades. Model-based software debugging (MBSD) is a particular technique that exploits discrepancies between a program execution and the intended behaviour to isolate program fragments that could potentially explain an observed misbehaviour. In contrast to other techniques, model-based debugging does not require a formal specification of a program's behaviour, making the approach suitable for developers without training in formal software engineering practices. A key aspect of model-based debugging is the transformation of the given program into a model suitable for debugging. In this thesis, several models for analysing programs written in an object-oriented language are investigated, with Java as concrete example. The aim of this work is to assess the suitability of value-based models and generalisations thereof for debugging of programs making use of dynamically allocated data structures, recursive methods and polymorphic method invocations.

As machines are increasingly used to fulfill even more needs of mankind, the dependence upon those machines increase. To prevent catastrophic failure and to facilitate maintenance a diagnostic system can be used. A diagnostic system supervises the system and can alarm the operator when a fault has occurred, and possibly determine what the cause may be. One architecture of a diagnostic system is a number of tests run by an on-board computer checking certain combinations of sensor values and control signals chosen in advance. To design these tests is a difficult task, which leads to the desire to automate the test construction. A part of this task can be performed using structural methods. In this thesis model based diagnosis is considered. This means that a formal mathematical model is used. The models typically consist of a number of equations describing the behavior of the system. In structural methods it is only considered if a variable exists in an equation or not. The goal of this master thesis project has been to apply structural methods to RODON models. RODON is a software diagnostics tool brought to market by Sörman Information &amp; Media, which can perform various diagnostic-related tasks based on a single model. This model is defined in an object oriented fashion using a Modelica-like language called Rodelica. A prototype implementation of a structural algorithm plug-in has been developed and integrated into RODON. An additional part of the project has been to investigate further possible uses of structural algorithms in RODON, apart from diagnostic test construction. This has been performed as a series of interviews with Sörman and university employees. The work performed in this thesis has shown that it is possible to apply structural methods to RODON models. It has also shown that even a prototype implementation can handle quite large systems. Some problems have been found as well, most notably in extracting a structural model from a RODON model. A consequence is that the developed structural plug-in only works for a subset of RODON models. It might be possible to deal with these problems if more time would be spent on the task. Finally, the interview survey revealed other possible uses of structural methods in RODON, including optimal sensor placement analysis and isolability and detectability analysis.

Legislation requires substantially lowered emissions and that all trucks manufactured are equipped with an On-Board Diagnosis (OBD) system. One approach for designing an OBD system is to use model based diagnosis and residual generation. At Scania CV AB, a method for automatic design of a diagnosis system from a model has been developed but there are still possibilities for improvements to get more and better residual generators. The main objective of this thesis is to analyze and improve the existing method. A theoretic outline of two methods using different causality assumptions is presented and the differences are analyzed and discussed. Stability of residual generators is analyzed and a method for constructing stable residual generators and its consequences for the diagnosis system is presented. Methods using integral and derivative causality are found not to be equivalent for all dynamic systems, resulting in that a diagnosis system utilizing both methods would be preferred for detectability reasons. A stable residual generator can be constructed from an unstable residual generator. The method for stabilizing a residual generator affects the fault sensitivity of the residual generator and the fault detectability properties of the diagnosis system. / Lagkrav kräver väsentligt sänkta emissionsnivåer och att alla tillverkade lastbilar är utrustade med ett system för On-Board Diagnosis (OBD). Ett sätt att konstruera ett OBD system är att använda modellbaserad diagnos och residualgenerering. På Scania CV AB har en metod för automatisk konstruktion av ett diagnossystem utifrån en modell utvecklats, men det finns utrymme för bättringar som leder till att fler och bättre residualgeneratorer konstrueras. Huvudsyftet med examensarbetet är att analysera och förbättra den existerande metoden. En teoretisk beskrivning av två metoder som använder sig av olika kausalitet presenteras och skillnaderna analyseras och diskuteras. Stabiliteten hos residualgeneratorer analyseras och en metod för att konstruera stabila residualgeneratorer och dess konsekvenser för diagnossystemet presenteras. Metoder som använder sig av integrerande respektive deriverande kausalitet visar sig inte vara ekvivalenta för alla dynamiska system, vilket resulterar i att ett diagnossystem som använder sig av båda kausaliteterna är att föredra i ett diagnossystem med avseende på detekterbarhet. En stabil residualgenerator kan konstrueras från en instabil residualgenerator. Metoden för att stabilisera en residualgenerator påverkar felkänsligheten hos residualgeneratorn och feldetekterbarheten hos diagnossystemet.

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