In this paper a stepwise information-theoretic feature selector is designed and implemented to reduce the dimension of a data set without losing pertinent information. The effectiveness of the proposed feature selector is demonstrated by selecting features from forty three variables monitored on a set of heavy duty diesel engines and then using this feature space for classification of faults in these engines. Using a cross-validation technique, the effects of various classification methods (linear regression, quadratic discriminants, probabilistic neural networks, and support vector machines) and feature selection methods (regression subset selection, RV-based selection by simulated annealing, and information-theoretic selection) are compared based on the percentage misclassification. The information-theoretic feature selector combined with the probabilistic neural network achieved an average classification accuracy of 90%, which was the best performance of any combination of classifiers and feature selectors under consideration.
Information-Theoretic Sensor Subset Selection: Application to Signal-Based Fault Isolation in Diesel Engines
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Joshi, AA, Meckl, PH, King, GB, & Jennings, K. "Information-Theoretic Sensor Subset Selection: Application to Signal-Based Fault Isolation in Diesel Engines." Proceedings of the ASME 2006 International Mechanical Engineering Congress and Exposition. Manufacturing Engineering and Textile Engineering. Chicago, Illinois, USA. November 5–10, 2006. pp. 277-286. ASME. https://doi.org/10.1115/IMECE2006-15903
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