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Authors: Indu Shukla ; Antoinette Silas ; Haley Dozier ; Brandon E. Hansen and W. Glenn Bond

Affiliation: US Army ERDC, Information Technology Laboratory, Vicksburg, MS, 39180, U.S.A.

Keyword(s): Long Short-Term Memory (LSTM), Vector Auto Regression (VAR), Prognostics and Health Management (PHM).

Abstract: This paper presents a data driven hybrid approach for Prognostics and Health Management (PHM) of military ground vehicles to mitigate a number of the unexpected failures, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. For military ground vehicles, the Controller Area Network (CAN) bus provides sensor data for collection and analysis. In this study we used collected operational time-series data for generating future operational time series data for military ground vehicles. Our sensor data share stochastic trends with more than one-time dependent variable to develop Vector AutoRegression (VAR) models suitable to forecast operational data. We have developed Long Short-Term Memory (LSTM) fault detection models which ingest VAR forecasted data to identify fault detection. Our experimental results show our hybrid approach provides promising fault diagnosis performance. Root mean squared error, mean absolute percentage error and mea n absolute error have been used as the evaluation criteria. (More)

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Paper citation in several formats:
Shukla, I.; Silas, A.; Dozier, H.; Hansen, B. and Bond, W. (2021). Data Driven Hybrid Approach for Health Monitoring and Fault Detection in Military Ground Vehicles. In Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-521-0; ISSN 2184-285X, SciTePress, pages 300-307. DOI: 10.5220/0010582603000307

@conference{data21,
author={Indu Shukla. and Antoinette Silas. and Haley Dozier. and Brandon E. Hansen. and W. Glenn Bond.},
title={Data Driven Hybrid Approach for Health Monitoring and Fault Detection in Military Ground Vehicles},
booktitle={Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA},
year={2021},
pages={300-307},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010582603000307},
isbn={978-989-758-521-0},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA
TI - Data Driven Hybrid Approach for Health Monitoring and Fault Detection in Military Ground Vehicles
SN - 978-989-758-521-0
IS - 2184-285X
AU - Shukla, I.
AU - Silas, A.
AU - Dozier, H.
AU - Hansen, B.
AU - Bond, W.
PY - 2021
SP - 300
EP - 307
DO - 10.5220/0010582603000307
PB - SciTePress

- Science and Technology Publications, Lda.
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