<p>The problem of fault diagnosis consists of detecting and isolating faults present in a system. As technical systems become more and more complex and the demands for safety, reliability and environmental friendliness are rising, fault diagnosis is becoming increasingly important. One example is automotive systems, where fault diagnosis is a necessity for low emissions, high safety, high vehicle uptime, and efficient repair and maintenance.</p><p>One approach to fault diagnosis, providing potentially good performance and in which the need for additional hardware is minimal, is model-based fault diagnosis with residuals. A residual is a signal that is zero when the system under diagnosis is fault-free, and non-zero when particular faults are present in the system. Residuals are typically generated by using a mathematical model of the system and measurements from sensors and actuators. This process is referred to as residual generation.</p><p>The main contributions in this thesis are two novel methods for residual generation. In both methods, systems described by Differential-Algebraic Equation (DAE) models are considered. Such models appear in a large class of technical systems, for example automotive systems. The first method consider observer-based residual generation for linear DAE-models. This method places no restrictions on the model, such as e.g. observability or regularity, in comparison with other previous methods. If the faults of interest can be detected in the system, the output from the design method is a residual generator, in state-space form, that is sensitive to the faults of interest. The method is iterative and relies on constant matrix operations, such as e.g. null-space calculations and equivalence transformations.</p><p>In the second method, non-linear DAE-models are considered. The proposed method belongs to a class of methods, in this thesis referred to as sequential residual generation, which has shown to be successful for real applications. This method enables simultaneous use of integral and derivative causality, and is able to handle equation sets corresponding to algebraic and differential loops in a systematic manner. It relies on a formal framework for computing unknown variables in the model according to a computation sequence, in which the analytical properties of the equations in the model as well as the available tools for equation solving are taken into account. The method is successfully applied to complex models of an automotive diesel engine and a hydraulic braking system.</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>

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.

The problem of fault diagnosis consists of detecting and isolating faults present in a system. As technical systems become more and more complex and the demands for safety, reliability and environmental friendliness are rising, fault diagnosis is becoming increasingly important. One example is automotive systems, where fault diagnosis is a necessity for low emissions, high safety, high vehicle uptime, and efficient repair and maintenance. One approach to fault diagnosis, providing potentially good performance and in which the need for additional hardware is minimal, is model-based fault diagnosis with residuals. A residual is a signal that is zero when the system under diagnosis is fault-free, and non-zero when particular faults are present in the system. Residuals are typically generated by using a mathematical model of the system and measurements from sensors and actuators. This process is referred to as residual generation. The main contributions in this thesis are two novel methods for residual generation. In both methods, systems described by Differential-Algebraic Equation (DAE) models are considered. Such models appear in a large class of technical systems, for example automotive systems. The first method consider observer-based residual generation for linear DAE-models. This method places no restrictions on the model, such as e.g. observability or regularity, in comparison with other previous methods. If the faults of interest can be detected in the system, the output from the design method is a residual generator, in state-space form, that is sensitive to the faults of interest. The method is iterative and relies on constant matrix operations, such as e.g. null-space calculations and equivalence transformations. In the second method, non-linear DAE-models are considered. The proposed method belongs to a class of methods, in this thesis referred to as sequential residual generation, which has shown to be successful for real applications. This method enables simultaneous use of integral and derivative causality, and is able to handle equation sets corresponding to algebraic and differential loops in a systematic manner. It relies on a formal framework for computing unknown variables in the model according to a computation sequence, in which the analytical properties of the equations in the model as well as the available tools for equation solving are taken into account. The method is successfully applied to complex models of an automotive diesel engine and a hydraulic braking system.