This report present the work to develop a misfire detection algorithm for onboard diagnostics on a spark ignited combustion engine. The work is based on a previous developed model-based detection algorithm, created to meet more stringent future legislation and reduce the cost of calibration. In the existing approach a simplified engine model is used to estimate the torque from the flywheel angular velocity, and the algorithm can detect misfires in various conditions. The main contribution in this work, is further development of the misfire detection algorithm with focus on improving the handling of disturbances and variations between different vehicles. The resulting detection algorithm can be automatically calibrated with training data and manage disturbances such as manufacturing errors on the flywheel and torsional vibrations in the crankshaft occurring after a misfire. Furthermore a robustness analysis with different engine configurations is carried out, and the algorithm is evaluated with the Kullback- Leibler divergence correlated to the diagnosis requirements. In the validation, data from vehicles with four cylinder engines are used and the algorithm show good performance with few false alarms and missed detections.

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.

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.

The parts in this report that are classified have been replaced by the symbol □.Even axes in some figures have been erased by the same reason.In emission legislation from California Air Resources Board, CARB, futuredemands on misfire detection in heavy duty diesel vehicles will cover anextended operating area than previous demands. Industry standard has so farbeen using crank shaft speed based methods. It is not obvious that speedbased methods will manage to detect misfire in the extended area.This report shows an alternative approach for misfire detection, based uponmeasured pressure in the exhaust manifold. The method compares themeasured pressure pattern with known templates. This work shows that themethod works at stationary measurements in the whole required operatingarea. Furthermore, measurements has shown that the exhaust brake can beused at pressures up to about □□□ hPa without any significant change to theexhaust pressure pattern. The influence of waste gate use at low load has beenanalysed. It shows low influence on the pressure trace when using misfiredetection template. Finally location of the pressure sensor has been studied,showing a possibility to improve misfire detection by placing the sensor closeto the exhaust port at the cylinder where improvement is needed. / De delar av denna rapport som är hemligstämplade har ersatts av symbolen□. Även axlar i vissa figurer har raderats av samma skäl.I emissionslagstiftning från California Air Resources Board, CARB, kommermisständningsdetektion för tunga dieselfordon att krävas i ett störrearbetsområde än vad som tidigare kravställts. Industristandard har hittillsvarit varvtalsbaserad misständningsdiagnos, men det är inte uppenbart attden metoden kommer att klara detektion i det utökade lagkravsområdet.Denna rapport visar en alternativ metod för misständningsdetektion sombaseras på uppmätt tryck i avgassamlaren. Metoden som används jämföruppmätt avgastryck mot ett känt mönster. Det har i detta arbete visats attmetoden fungerar vid stationära mätningar i hela lagkravsområdet. Vidare harmätningar visat att avgasbroms kan användas upp till ca □□□ hPa mottryckutan att tryckmönstret ändras signifikant. Inverkan av turbons waste gate vidlåga laster har analyserats och visar låg påverkan i fall då man användermisständning som detektionsmönster. Slutligen har placering avtrycksensorer analyserats. Denna visar att det går att förbättradetektionsförmågan genom att placera sensorn närmare utblåset på dencylinder man önskar förbättra detektionen.