Advanced misfire detection using adaptive signal processing

Abstract

This article addresses the problem of internal combustion engine misfire detection. Specific attention is paid to misfire detection using crankshaft speed measurements. After an initial analysis of the problem, the applicability of advanced signal processing methods to its solution is discussed. We demonstrate how adaptive filtering techniques can lead to improved misfire detection accuracy. This can lead to reduced misdetection as well as false alarm rates. The general concept of estimating and removing periodic, timevarying components may also be applied to other automotive problems.

Diagnosability performance analysis of models and fault detectors

“…These methods usually require additional sensors that are not generally available in the vehicle. Therefore, an available signal that is commonly used to detect misfires is the crankshaft angular velocity measured at the flywheel (Williams, 1996;Osburn et al, 2006;Naik, 2004;Tinaut et al, 2007).…”

“…The punched holes give an angular resolution of 6 • , however, two following holes are removed resulting in 58 punched holes, where the longer interval without holes is used to keep track of the flywheel angle. Sometimes, the flywheel signal is "down-sampled" by measuring the elapsed time for longer angular intervals (Naik, 2004;Osburn et al, 2006). …”

Development of misfire detection algorithm using quantitative FDI performance analysis

“…• [20] and 90 • [19]). High resolution data gives more information about each combustion but requires more computational power and is also more sensitive to measurement errors caused by flywheel manufacturing errors and signal sampling resolution [19].…”

“…The misfire detection performance varies with different operating points, such as load and speed, and is also affected by disturbances such as: cycle-to-cycle and cylinder-to-cylinder variations, driveline oscillations, and flywheel manufacturing errors, see [19]. A contribution in this work is an analysis of measurement data, from different vehicles with the same type of engine, to see how misfire detection performance varies for different loads and speeds but also, in contrast to previous works, other complicating circumstances such as cold starts.…”

“…An overview of misfire detection research can be found in [17] and the most common used signal for misfire de- Signal analysis approaches used in, e.g., [19] and [20], utilize the oscillations in the angular velocity signal, related to the cylinder combustions, to detect when one or several cylinders fail to fire. In [24], a Markov chainbased algorithm is proposed to detect misfires.…”