Automotive diagnostics using trainable classifiers: Statistical testing and paradigm selection

Abstract

An analysis is presented of the requirements for developing a practical trainable classifier to detect and identify faults in vehicle powertrain systems. An examination is made of requirements on the data sets used for training and testing and the criteria needed to select the most appropriate classifier for a particular family of problems. Empirical results supporting the authors' hypothesis are presented based on an analysis of two data sets drawn under rather different circumstances from test vehicles with faults introduced. Several different classifier forms are applied to these data sets, and their performance is evaluated. Despite similar performance on simple statistical tests, the classifiers exhibit significant performance variations on more rigorous tests, and therefore viable criteria for selecting the most appropriate classifiers can be established.