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

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Published Nov 5, 2024
Ethan Kohrt Matthew Moon Matthew Sullivan Sri Das Michael Thurston Nenad G. Nenadic

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.

How to Cite

Kohrt, E., Moon, M. ., Sullivan, M., Das, S., Thurston, M., & Nenadic, N. G. (2024). Data-driven Detection of Engine Faults in Infrequently-driven Ground Vehicles. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.3899
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Keywords

Anomaly detection, Autoencoders, Neural networks, Engine faults, Ground vehicles, Fleet

References
Eklund, N. (2018). Anomaly detection tutorial. In Proceedings of the annual conference of the prognostics and
health management society.
Japkowicz, N., Myers, C., & Gluck, M. (1995). A novelty detection approach to classification. In Ijcai (Vol. 1, pp. 518–523).
Yan, W., & Yu, L. (2015). On accurate and reliable anomaly detection for gas turbine combustors: A deep learning approach. In Proceedings of the annual conference of the prognostics and health management society
Section
Technical Research Papers