Stronger environmental awareness as well as actual and future legislations increase the demands on diagnosis and supervision of any vehicle with a combustion engine. Particularly this concerns heavy duty trucks, where it is common with long driving distances and large engines. Model based diagnosis is an often used method in these applications, since it does not require any hardware redundancy. Undesired changes in the intake manifold pressure can cause increased emissions. In this thesis a diagnosis system for supervision of the intake manifold pressure is constructed and evaluated. The diagnosis system is based on a Mean Value Engine Model (MVEM) of the intake manifold pressure in a diesel engine with Exhaust Gas Recirculation (EGR) and Variable Geometry Turbine (VGT). The observer-based residual generator is a comparison between the measured intake manifold pressure and the observer based estimation of this pressure. The generated residual is then post treated in the CUSUM algorithm based diagnosis test. When constructing the diagnosis system, robustness is an important aspect. To achieve a robust system design, four different observer approaches are evaluated. The four approaches are extended Kalman filter, high-gain, sliding mode and an adaption of the open model. The conclusion of this evaluation is that a sliding mode approach is the best alternative to get a robust diagnosis system in this application. The CUSUM algorithm in the diagnosis test improves the properties of the diagnosis system further.

New emission legislations introduced in the European Union and the U.S. have made truck manufacturers face stricter requirements for low emissions and on-board diagnostic systems. The on-board diagnostic system typically consists of several tests that are run when the truck is driving. One way to construct such tests is to use so called consistency relations. A consistency relation is a relation with known variables that in the fault free case always holds. Calculation of a consistency relation typically involves eliminating unknown variables from a set of equations. To eliminate variables from a differential polynomial system, methods from differential algebra can be used. In this thesis, the purely algebraic Gröbner basis algorithm and the differential Rosenfeld-Gröbner algorithm implemented in the Maple package Diffalg have been compared and evaluated. The conclusion drawn is that there are no significant differences between the methods. However, since using Gröbner basis requires differentiations to be made in advance, the recommendation is to use the Rosenfeld-Gröbner algorithm. Further, attempts to calculate consistency relations using the Rosenfeld-Gröbner algorithm have been made to a real application, a model of a Scania diesel engine. These attempts did not yield any successful results. It was only possible to calculate one consistency relation. This can be explained by the high complexity of the model.

Technical systems of today are often complex and integrated. If a fault occurs, the consequences can be disastrous both for the system itself and its surroundings. To maintain the operation and the security it is necessary to have a surveillance system which can detect a fault in an early stage. In this thesis two structural methods for fault isolation analysis are discussed. The result from the studied algorithms shows what fault isolation properties a diagnostic model is expected to have. If the isolability is not good enough, it also gives information on where further modelling needs to be done. To base a comparison of the two structural analysis algorithms on, four criteria are defined concerning for example realizability of residuals and time complexity. One interesting part of the methods is how dynamic models are handled. It is shown how differential constraints can end up in differential cycles which implies calculatory problems and what effects structural differentiation has on a system. The algorithms have been tested on an application from the research training network DAMADICS. The result shows how different types of input models in this case give the same result.

Diagnosis is the task of finding faults or malfunctioning components in a technical system, e.g a car. When doing diagnosis on cars with combustion engines, a computer program can be used. The computer program, also called diagnosis system, needs information about the car. This information could be data sheets of all the electronic components in the car. It could also be a description of how the engine behaves in a nominal and a non-nominal case. This information is contained in a model of the engine. RODON, a diagnostic tool developed by Sörman Information and Media AB, uses models of systems for conflict detection diagnosis. RODON needs fault models of the components to do diagnosis. The diagnosis system is then used in workshops, factories, or other places where cars need to be surveyed. In this thesis, a Simulink model of the nominal behaviour of a combustion engine is given. The problem is how to make use of the model as well as the diagnostic tool RODON for combustion engine diagnosis. To solve this, the Simulink model is translated into a RODON model. Translating a Simulink model into a RODON model requires a new library in RODON. The library developed in this thesis is called AdvancedBlocks library. The Simulink model describes the nominal behaviour of a combustion engine but for diagnosis with RODON, fault models are needed as well. Several types of faults that can occur in an engine have been studied and fault models have been implemented in RODON. The conclusion is that diagnosis in RODON with a translated engine model is possible.

Type 2 diabetes has reached epidemic proportions worldwide. The resulting increase in chronic and costly diabetes related complications has potentially catastrophic implications for healthcare systems, and economies and societies as a whole. One of the key pathological factors leading to type 2 diabetes is insulin resistance (IR), which is the reduced or impaired ability of the body to make use of available insulin to maintain normal blood glucose levels. Diagnosis of developing IR is possible up to 10 years before the diagnosis of type 2 diabetes, providing an invaluable opportunity to intervene and prevent or delay the onset of the disease. However, an accurate, yet simple, test to provide a widespread clinically feasible early diagnosis of IR is not yet available. Current clinically practicable tests cannot yield more than a crude surrogate metric that allows only a threshold-based assessment of an underlying disorder, and thus delay its diagnosis. This thesis develops, analyses and pilots a model-based insulin sensitivity test that is simple, short, physiological and cost efficient. It is thus useful in a practical clinical setting for wider clinical screening. The method incorporates physiological knowledge and modelling of glucose, insulin and C-peptide kinetics and their pharmaco-dynamics. The clinical protocol is designed to produce data from a dynamic perturbation of the metabolic system that enables a unique physiologically valid assessment of metabolic status. A combination of a-priori information and a convex integral-based identification method guarantee a unique, robust and automated identification of model parameters. In addition to a high resolution insulin sensitivity metric, the test also yields a clinically valuable and accurate assessment of pancreatic function, which is also a good indicator of the progression of the metabolic defect. The combination of these two diagnostic metrics allow a clinical assessment of a more complete picture of the overall metabolic dysfunction. This outcome can assist the clinician in providing an earlier and much improved diagnosis of insulin resistance and metabolic status and thus more optimised treatment options. Test protocol accuracy is first evaluated in Monte Carlo simulations and subsequently in a clinical pilot study. Both validations yield comparable results in repeatability and robustness. Repeatability and resolution of the test metrics are very high, particularly when compared to current clinical standard surrogate fasting or oral glucose tolerance assessments. Additionally, the model based insulin sensitivity metric is shown to be highly correlated to the highly complex, research focused gold standard euglycaemic clamp test. Various reduced sample and shortened protocols are also proposed to enable effective application of the test in a wider range of clinical and laboratory settings. Overall, test time can be as short as 30 minutes with no compromise in diagnostic performance. A suite of tests is thus created and made available to match varying clinical and research requirements in terms of accuracy, intensity and cost. Comparison between metrics obtained from all protocols is possible, as they measure the same underlying effects with identical model-based assumptions. Finally, the proposed insulin sensitivity test in all its forms is well suited for clinical use. The diagnostic value of the test can assist clinical diagnosis, improve treatment, and provide for higher resolution and earlier diagnosis than currently existing clinical and research standards. High risk populations can therefore be diagnosed much earlier and the onset of complications delayed. The net result will thus improve overall healthcare, reduce costs and save lives.

Failure detection and isolation (FDI) is essential for reliable operations of complex autonomous systems or other systems where continuous observation or maintenance thereof is either very costly or for any other reason not easily accessible. Beneficial for the model based FDI is that there is no need for fault data to detect and isolate a fault in contrary to design by data clustering. However, it is limited by the accuracy and complexity of the model used. As models grow more complex, or have multiple interconnections, problems with the traditional methods for FDI emerge. The main objective of this thesis is to utilise the automated methodology presented in [Svärd, 2012] to create a model based FDI system for the Columbus air loop. A small but crucial part of the life support on board the European space laboratory Columbus. The process of creating a model based FDI, from creation of the model equations, validation thereof to the design of residuals, test quantities and evaluation logic is handled in this work. Although the latter parts only briefly which leaves room for future work. This work indicate that the methodology presented is capable to create quite decent model based FDI systems even with poor sensor placement and limited information of the actual design. [] Carl Svärd. Methods for Automated Design of Fault Detection and Isolation Systems with Automotive Applications. PhD thesis, Linköping University, Vehicular Systems, The Institute of Technology, 2012

Underwater robotic vehicles are used in a variety of environments that would be dangerous for humans. For these vehicles to be successful, they need to be tolerant of a variety of internal and external faults. To be resilient to internal faults, the system must be capable of determining the source of faulty behavior. However many different faults within a robotic vehicle can create identical faulty behavior, which makes the vehicles impossible to diagnose using conventional methods. I propose a novel active diagnosis method for differentiating between faults that would otherwise have identical behavior. I apply this method to a communication system and a power distribution system in a robotic vehicle and show that active diagnosis is successful in diagnosing partially observable faults. An example of an external fault is inter-robot communication in underwater robotics. The primary communication method for underwater vehicles is acoustic communication which relies heavily on line-of-sight tracking and range. This can cause severe packet loss between agents when a vehicle is operating around obstacles. I propose novel path-planning methods for an Autonomous Underwater Vehicle (AUV) that ferries messages between agents. I applied this method to a custom underwater simulator and illustrate how it can be used to preserve at least twice as many packets sent between agents than would be obtained using conventional methods.

<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>

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