Independent thesis Advanced level (professional degree), 20 credits / 30 HE credits
In the heavy vehicle industry customers, laws and increasingly complex processes demand methods of supervising every aspect of a truck. Fault isolation systems are introduced to do just that. In order to assure a sustainable development new types of isolation systems are investigated to substitute the consistency based isolation systems of today.
In this thesis an application of a probabilistic isolation method that ranks possible faults on their likeliness of being a fault in the process is implemented and evaluated as a possible future replacement of today's system. This method bases the isolation on training data collected from measurements on the process and observation of the process.
The probabilistic isolation method is evaluated on hos it performs under different circumstances such as the effort of different amounts of training data and how well it performs if the tests and observations of the process are of varying quality.
Solution to several problems that arise when this method is implemented are also investigated such as how the system handles cases where several faults occur at the same time, what happens if there are missing data in the observations of the system and how to solve problems that involve execution times which is important in embedded systems.
The results that are derived show that this probabilistic isolation system performs well on the process as it is today and that this is a good substitute when developing for future processes. There is however a need for further development of the system such as improved isolation when there are several faults present in the process and questions on how to collect and store the training data still remain to be answered. A full scale implement would allow for better comparison with the current system and give more information on runtime and storage problems.
2008. , 84 p.