Daniel E. Jung

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— A number of residual generation methods have been developed for robust model-based fault detection and isolation (FDI). There have also been a number of offline (i.e., design-time) methods that focus on optimizing FDI performance (e.g., trading off detection performance versus cost). However, design-time algorithms are not tuned to optimize performance(More)
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:(More)
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(More)
This paper discusses a distributed diagnosis approach , where each subsystem diagnoser operates independently without a coordinator that combines local results and generates the correct global diagnosis. In addition, the distributed diagnosis algorithm is designed to minimize communication between the subsystems. A Minimal Structurally Overdetermined (MSO)(More)
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