Analysis and optimization with the Kullback-Leibler divergence for misfire detection using estimated torque
Engine misfire detection is an important part of the On-Board Diagnostics (OBDII)
legislations to reduce exhaust emissions and avoid damage to the catalytic converters. The
flywheel angular velocity signal is analyzed, investigating how to use the signal in order
to best detect misfires. An algorithm for engine misfire detection is proposed based on
the flywheel angular velocity signal. The flywheel signal is used to estimate the torque at
the flywheel and a test quantity is designed by weighting and thresholding the samples of
estimated torque related to one combustion. During the development process, the Kullback-
Leibler divergence is used to analyze the ability to detect a misfire given a test quantity
and how the misfire detectability performance varies depending on, e.g., load and speed.
The Kullback-Leibler divergence is also used for parameter optimization to maximize the
difference between misfire data and fault-free data. Evaluation shows that the proposed
misfire detection algorithm is able to have a low probability of false alarms while having a
low probability of missed detections.
Daniel Eriksson, Lars Eriksson, Erik Frisk and Mattias Krysander
2013
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Last updated: 2021-11-10
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