For achieving high efficiency and low exhaust emissions, engines need to be
operated near the limits of stable combustion, such as lean or exhaust gas
recirculation (EGR) conditions. Sensing technologies of the combustion state by
existing engine components are of high interest. And the utilization of voltage
and current signals from ignition coils is discussed in this article. The
discharge channel of an ignition spark is strongly affected by flow variation
and spark plug surface conditions, and the behavior of discharge channel
stretching and restrike event can vary greatly from cycle to cycle. As a result,
the effects of flow velocity, temperature, pressure, and electrode surface
resistance are compounded in the voltage-current response, making it difficult
to accurately detect the combustion state for each cycle by a threshold judgment
process using a single feature value.
In this article, a method for inductively detecting misfires from voltage and
current signals of ignition coils by applying deep learning image recognition is
introduced. First, post-ignition for misfire detection is performed on the
engine bench during the expansion stroke in an engine cycle, when the cylinder
pressure is expected to differ between the combustion cycle and the ignition
cycle, and the ignition coil voltage and current are measured. Next, a
two-dimensional frequency distribution of voltage and current (discharge
histogram) is created as an input image for deep learning, and the AlexNet
model, which has been trained with more than one million images, is trained with
images of the ignition and combustion cycles as a supervised learning. The
accuracy of classification is then verified using a validation dataset. In
addition, to making the deep learning model more explainable, the activation
score distribution on the discharge histogram was visualized when the trained
model judges the images, and the discharge characteristics that provided the
basis for deep learning classifications were analyzed.
