Misfire-misfuel classification using support vector machines

Abstract

This paper proposes the use of support vector machines to perform classification between different types of missed combustion event in a six-cylinder engine. On-board diagnostics regulations require the detection of missed combustion events, which is possible through interpretation of crankshaft speed information. However, current approaches provide no information on the actual cause of the event, in particular whether it was caused by a misfuel (absence of fuel) or a misfire (absence of spark) event. Whilst the impact on the environment and emission treatment systems due to misfuel is minimal, misfire events are detrimental to both. Consequently information regarding the causes of missing c..