Improving Misfire Detection Using Gaussian Processes and Flywheel Error Compensation
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 eval- uated.
The improvements evaluated are: using Gaussian processes to model the
clas- sifier, 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
perfor- mance 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 re-
stricted EKF gives a vastly reduced computational load for only a
moderate loss of performance.
Gustav Romeling
2016
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Senast uppdaterad: 2021-11-10
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