Henningsson, M., Bernhardsson, B., Tunestal, P., and Johansson, R., "A Machine Learning Approach to Information Extraction from Cylinder Pressure Sensors," SAE Technical Paper 2012-01-0440, 2012, doi:10.4271/2012-01-0440.
As the number of actuators and sensors increases in modern combustion engines, the task of optimizing engine performance becomes increasingly complex. Efficient information processing techniques are therefore important, both for off-line calibration of engine maps, and on-line adjustments based on sensor data.In-cylinder pressure sensors are slowly spreading from laboratory use to production engines, thus making data with high temporal resolution of the combustion process available. The standard way of using the cylinder pressure data for control and diagnostics is to focus on a few important physical features extracted from the pressure trace, such as the combustion phasing CA50, the indicated mean effective pressure IMEP, and the ignition delay. These features give important information on the combustion process, but much information is lost as the information from the high-resolution pressure trace is condensed into a few key parameters.The final objective of engine calibration and control is to achieve low fuel consumption and emissions, and high reliability and durability of the engine. In light of this objective, it is proposed to approach the problem of extracting key features from the cylinder pressure data in a more systematic way. Here, a method is suggested to extract low-dimensional features from the high-dimensional pressure data such that the information retained is maximized. This leads to a principal component analysis approach. It is shown that only a few components are required to accurately describe the pressure trace.The low-dimensional principal component coefficients are then used as input to a neural network that can be trained to predict engine outputs of interest. It is shown that NOx and λ can be accurately predicted using the principal components coefficients and the neural network. Benefits of the scheme during transients are illustrated, where conventional sensors are too slow to provide cycle-to-cycle measurements.