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

Scania CV AB is a leading company within development and production of buses, trucks as well as industrial and marine engines. New environmental and safety legislations continuously demand higher quality from the products. An upcoming European legislation, Euro 6, implies that gas leakages from truck engines should be detected while driving. If the source of the leakage is not only detected, but also isolated, that is separated from other faults, the adjustments in the workshop goes faster since there is no need for leakage localisation. A faster reparation increases the up-time, i.e. the amount of time that the truck can be used. This master thesis work uses current methods developed at Scania for residual generation to perform model-based leakage diagnosis. In this work, measurements are gathered for dierent sensor faults and two leakages. The measurements are used to evaluate the actual performance of the resulting diagnosis system. The result, based on the residuals generated by the method, shows that leakages on the boost-side and the exhaust-side can be detected, and isolated from faults in the pressure sensors on the boost-side and the exhaust-side. The isolation of these four faults is considered the hardest to achieve among sensor faults and leakages why the full isolation performance is promising. Further measurements are needed to determine the full isolation performance of the diagnosis system. The resulting system is reasoned to be suitable for execution in real time on-board the truck. / Scania CV AB är en ledande koncern inom utveckling och produktion av bussar, lastbilar samt industri- och marinmotorer. Nya lagkrav för miljö och säkerhet ställer ständigt högre krav på de tillverkade produkterna. Ett nära förestående lagkrav för lastbilar, Euro 6, innebär att gasläckage från motorn ska detekteras under körning. Om läckaget förutom att detekteras också kan isoleras, det vill säga särskiljas från andra fel, går reparationen i verkstaden snabbare då man slipper lokalisera läckaget. En snabbare reparation ökar up-time, det vill säga tiden som lastbilen kan användas på åkeriet. I detta exjobb används befintliga metoder för residualgenerering framtagna på Scania för att åstadkomma modelbaserad läckagediagnos. Arbetet tar även fram mätdata för olika givarfel samt för två läckage i motorn. Denna mätdata används för att utvärdera det erhållna diagnossystemets faktiska prestanda. Resultatet, som bygger på residualerna som metoden genererat, visar att läckage går att detektera, och att läckagen går att isolera från fel på tryckgivarsensorer på laddluftssidan och avgassidan. Denna isolering anses vara den svåraste att uppnå av alla sensorfel samt läckage varvid övrig isoleringsprestande verkar lovande. Däremot behövs mer mätdata för att säkert kunna fastställa övrig isoleringsprestanda. Diagnosmetoden lämpar sig troligen för exekvering i realtid ombord på lastbilen.

Models are of great interest in many fields of engineering as they enable prediction of a systems behaviour, given an initial mode of the system. However, in the field of model-based diagnosis the models are used in a reverse manner, as they are combined with the observations of the systems behaviour in order to estimate the system mode. This thesis describes computation of diagnostic systems based on models implemented in Dymola. Dymola is a program that uses the language Modelica. The Dymola models are translated to Matlab, where an application called Fault Diagnosis Toolbox, FDT is applied. The FDT has functionality for pinpointing minimal overdetermined sets of equations, MSOs, which is developed further in this thesis. It is shown that the implemented algorithm has exponential time complexity with regards to what level the system is overdetermined,also known as the degree of redundancy. The MSOs are used to generate residuals, which are functions that are equal to zero given that the system is fault-free. Residual generation in Dymola is added to the original methods of the FDT andthe results of the Dymola methods are compared to the original FDT methods, when given identical data. Based on these tests it is concluded that adding the Dymola methods to the FDT results in higher accuracy, as well as a new way tocompute optimal observer gain. The FDT methods are applied to 2 models, one model is based on a system ofJAS 39 Gripen; SECS, which stands for Secondary Enviromental Control System. Also, applications are made on a simpler model; a Two Tank System. It is validated that the computational properties of the developed methods in Dymolaand Matlab differs and that it therefore exists benefits of adding the Dymola implementations to the current FDT methods. Furthermore, the investigation of the potential isolability based on the current setup of sensors in SECS shows that full isolability is achievable by adding 2 mass flow sensors, and that the isolability is not limited by causality constraints. One of the found MSOs is solvable in Dymola when given data from a fault-free simulation. However, if the simulation is not fault-free, the same MSO results in a singular equation system. By utilizing MSOs that had no reaction to any modelled faults, certain non-monitored faults is isolated from the monitored ones and therefore the risk of false alarms is reduced. Some residuals are generated as observers, and a new method for constructing observers is found during the thesis by using Lannerheds theorem in combination with Pontryagin’s Minimum Priniple. This method enables evaluation of observer based residuals in Dymola without any selection of a specific operating point, as well as evaluation of observers based on high-index Differential Algebraic Equations, DAEs. The method also results in completely different behaviourof the estimation error compared to the method that is already implemented inthe FDT. For example, one of the new observer-implementations achieves both an estimation error that converges faster towards zero when no faults are implementedin the monitored system, and a sharper reaction to implemented faults.

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

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

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>Several different air pollutions are produced during combustion in a diesel engine, for example nitric oxides, NOx, which can be harmful for humans. This has led to stricter emission legislations for heavy duty trucks. The law requires both lower emissions and an On-Board Diagnosis system for all manufactured heavy duty trucks. The OBD system supervises the engine in order to keep the emissions below legislation demands. The OBD system shall detect malfunctions which may lead to increased emissions. To design the OBD system an automatic model based diagnosis approach has been developed at Scania CV AB where residual generators are generated from an engine model.</p><p>The main objective of this thesis is to improve the existing methods at Scania CV AB to extract residual generators from a model in order to generate more residual generators. The focus lies on the methods to find possible residual generators given an overdetermined subsystem. This includes methods to estimate derivatives of noisy signals.</p><p>A method to use both integral and derivative causality has been developed, called mixed causality. With this method it has been shown that more residual generators can be found when designing a model based diagnosis system, which improves the fault isolation. To use mixed causality, derivatives are estimated with smoothing spline approximation.</p>

Several different air pollutions are produced during combustion in a diesel engine, for example nitric oxides, NOx, which can be harmful for humans. This has led to stricter emission legislations for heavy duty trucks. The law requires both lower emissions and an On-Board Diagnosis system for all manufactured heavy duty trucks. The OBD system supervises the engine in order to keep the emissions below legislation demands. The OBD system shall detect malfunctions which may lead to increased emissions. To design the OBD system an automatic model based diagnosis approach has been developed at Scania CV AB where residual generators are generated from an engine model. The main objective of this thesis is to improve the existing methods at Scania CV AB to extract residual generators from a model in order to generate more residual generators. The focus lies on the methods to find possible residual generators given an overdetermined subsystem. This includes methods to estimate derivatives of noisy signals. A method to use both integral and derivative causality has been developed, called mixed causality. With this method it has been shown that more residual generators can be found when designing a model based diagnosis system, which improves the fault isolation. To use mixed causality, derivatives are estimated with smoothing spline approximation.

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.