Input Selection for Modeling and Diagnostics With Application to Diesel Engines

Abstract

A theoretical and experimental approach to the use of information theory in input space selection for modeling and diagnostic applications is examined. The assumptions and test cases used throughout the paper are specifically tailored to diesel engine diagnostic and modeling applications. This work seeks to quantify the amount of information about an output contained within an input space. The information theoretic quantity, conditional entropy, is shown to be an accurate predictor of model and diagnostic algorithm performance and therefore is a good choice for an input vector selection metric. Methods of estimating conditional entropy from collected data, including the amount of needed data, are also discussed.