Data-driven Detection of Engine Faults in Infrequently-driven Ground Vehicles

Abstract

We investigated the detection of engine faults in infrequently driven ground vehicles using data-driven methods based on neural network autoencoders. Multivariate time-series data from the infrequently driven vehicles under investigation had limited coverage of operating conditions. Hence, a considerable part of this work focused on identifying suitable vehicles, relevant signals, and pre-processing the data. We trained autoencoder models on eight vehicles with known faults and detected faults in six. Four of the faults were detectable under idle conditions and four were detectable under driving conditions. Model evaluations required human inspection to distinguish fault detections from other anomalies. We detail our procedures for pre-processing, model development, and post-processing, and we include a discussion on our interpretations of the model results.