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Failure Prognostics for In-Tank Fuel Pumps of the Returnless Fuel Systems

[+] Author Affiliations
Ehsan Taheri, Ilya Kolmanovsky

University of Michigan, Ann Arbor, MI

Oleg Gusikhin

Ford Research & Advanced Engineering, Dearborn, MI

Paper No. DSCC2016-9725, pp. V001T12A002; 10 pages
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5069-5
  • Copyright © 2016 by ASME


With the motivation to develop Condition Based Maintenance (CBM) strategies for the automotive vehicles, this paper considers a data-driven approach to the prognostics of the automotive fuel pumps. Focusing on the returnless type fuel delivery systems, our approach is based on estimating the fuel pump workload based on the model learned from the past driving history. Statistical reliability models are then exploited to estimate failure probability. These models are formulated in terms of the workload and updated from data available from vehicles in the field. Numerical examples which illustrate the proposed methodology are reported. Compared to alternative approaches, which are based on detailed physics-based degradation modeling and/or electrical signal analysis, our approach is data-driven, exploits connected vehicle analytics and reliability-based modeling, and has a potential to lead to simpler implementations.

Copyright © 2016 by ASME



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