The area of misfire detection is important because of the effects of misfires on both the environment and the exhaust system. Increasing requirements on the detection performance means that improvements are always of interest. In this thesis, potential improvements to an existing misfire detection algorithm are evaluated. The improvements evaluated are: using Gaussian processes to model the classifier, alternative signal treatments for detection of multiple misfires, and effects of where flywheel tooth angle error estimation is performed. The improvements are also evaluated for their suitability for use on-line. Both the use of Gaussian processes and the detection of multiple misfires are hard problems to solve while maintaining detection performance. Gaussian processes most likely loses performance due to loss of dependence between the weights of the classifier. It can give performance similar to the original classifier, but with greatly increased complexity. For multiple misfires, the performance can be slightly improved without loss of single misfire performance. Greater improvements are possible, but at the cost of single misfire performance. The decision is in the end down to the desired trade-off. The flywheel tooth angle error compensation gives nearly identical performance regardless of where it is estimated. Consequently the error estimation can be separated from the signal processing, allowing the implementation to be modular. Using an EKF for estimating the flywheel errors on-line is found to be both feasible and give good performance. Combining the separation of the error estimation from the signal treatment with a, after initial convergence, heavily restricted EKF gives a vastly reduced computational load for only a moderate loss of performance.

Model-based diagnosis compares observations from a system with predictions using a mathematical model to detect and isolate faulty components. Analyzing which faults that can be detected and isolated given the model gives useful information when designing a diagnosis system. This information can be used, for example, to determine which residual generators can be generated or to select a sufficient set of sensors that can be used to detect and isolate the faults. With more information about the system taken into consideration during such an analysis, more accurate estimations can be computed of how good fault detectability and isolability that can be achieved. Model uncertainties and measurement noise are the main reasons for reduced fault detection and isolation performance and can make it difficult to design a diagnosis system that fulfills given performance requirements. By taking information about different uncertainties into consideration early in the development process of a diagnosis system, it is possible to predict how good performance can be achieved by a diagnosis system and avoid bad design choices. This thesis deals with quantitative analysis of fault detectability and isolability performance when taking model uncertainties and measurement noise into consideration. The goal is to analyze fault detectability and isolability performance given a mathematical model of the monitored system before a diagnosis system is developed. A quantitative measure of fault detectability and isolability performance for a given model, called distinguishability, is proposed based on the Kullback-Leibler divergence. The distinguishability measure answers questions like "How difficult is it to isolate a fault fi from another fault fj?. Different properties of the distinguishability measure are analyzed. It is shown for example, that for linear descriptor models with Gaussian noise, distinguishability gives an upper limit for the fault to noise ratio of any linear residual generator. The proposed measure is used for quantitative analysis of a nonlinear mean value model of gas flows in a heavy-duty diesel engine to analyze how fault diagnosability performance varies for different operating points. It is also used to formulate the sensor selection problem, i.e., to find a cheapest set of available sensors that should be used in a system to achieve required fault diagnosability performance. As a case study, quantitative fault diagnosability analysis is used during the design of an engine misfire detection algorithm based on the crankshaft angular velocity measured at the flywheel. Decisions during the development of the misfire detection algorithm are motivated using quantitative analysis of the misfire detectability performance showing, for example, varying detection performance at different operating points and for different cylinders to identify when it is more difficult to detect misfires. This thesis presents a framework for quantitative fault detectability and isolability analysis that is a useful tool during the design of a diagnosis system. The different applications show examples of how quantitate analysis can be applied during a design process either as feedback to an engineer or when formulating different design steps as optimization problems to assure that required performance can be achieved.

In this thesis the possibility of using knock sensors for misfire detection in heavy duty diesel engines is investigated. This is of great interest due to many emission legislations getting stricter, especially in the US where robust misfire detection is mandatory for these types of engines. In order to capture the relevant vibra- tions on the engine a pre-study is made where the resonance frequencies in the cylinders are calculated which can be used for bandpass filtering the knock sen- sor signal. These bandpass filters are used to run tests where the engine is run with normal combustion and misfire on individual cylinders. The tests are made using a straight six cylinder diesel engine with a displacement of 12.7 litres. The test data is used to create threshold maps that can be used for misfire detection and for sensitivity analysis. Thereafter a detection algorithm is developed in MATLAB which involves integrating the knock sensor signal between two prede- fined crank angle degrees and then comparing it with a threshold value that can be interpolated from the threshold map. The test results show that this type of detection algorithm with these types of sensors is possible. There are also some areas of improvements presented that can make the misfire detection even more robust. / I denna rapport undersöks möjligheten att använda knackgivare för misständ- ningsdetektion i dieselmotorer för kommersiellt bruk. Bakgrunden är allt sträng- are lagstiftning i framförallt USA där det ställs krav på robust detektion av miss- tändningar i motorer för kommersiellt bruk. För att fånga de relevanta frekven- serna i cylindrarna som kan användas för detektion av misständning görs en för- studie där resonansfrekvenser beräknas och sedan används för att skapa band- passfilter. Filterna kan i sin tur användas för att filtrera knackgivarsignalerna. De framtagna bandpassfilterna används sedan för omfattande testning av miss- tändning i testcell. Motorn som används i testningen är en rak sexcylindrig die- selmotor med 12.7 liters slagvolym. Med den insamlade datan görs en känslig- hetsanalys samt gränsvärdesmappar som kan användas för misständsdetektion. Därefter utvecklas en detektionsalgoritm i MATLAB som går ut på att integrera knackgivarsignalen mellan två vevaxelvinklar och sedan jämföra med ett gräns- värde som kan interpoleras fram från gränsvärdesmappen. Testdatan visar att detektion med denna typ av system och med denna typ av algoritm är möjlig. I rapporten föreslås också ett antal områden som skulle kunna göra systemet ännu mer robust.