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Data-driven modeling of truck engine exhaust valve failures: A case study

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Abstract

Exhaust valve is an essential part of truck engine. Dynamic and unpredictable thermal and mechanical stress cause valves to wear prematurely, leading to increased maintenance costs. In this paper, a data-driven approach is presented to predict failures of exhaust valves of truck engines. The failure datasets of exhaust valves recorded from 13 truck engines are divided into three groups: First failure, second failure, and third or more failures. The Kaplan-Meier estimator is selected to express the distribution of survival probability of the three groups of failures. In order to find the hazard indicator, two data-mining algorithms, a wrapper and a boosting tree are applied to select parameters highly relevant to the hazard rate. A Cox proportional hazard model is used to conduct regression analysis on each selected parameter. Based on the derived hazard ratio, the time-dependent baseline hazard rate is computed. Five parametric reliability models are selected to capture the baseline hazard rate for the three groups. The value-at-risk for each group of failures is computed to express the risk at different confidence levels. Life circle of truck engine exhaust valves can be estimated.

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA, 52242, USA

    Yusen He & Andrew Kusiak

  2. School of Electrical Engineering, Wuhan University, Wuhan, 430072, China

    Tinghui Ouyang

  3. School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing, 102206, China

    Wei Teng

Authors
  1. Yusen He
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  2. Andrew Kusiak
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  3. Tinghui Ouyang
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  4. Wei Teng
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Corresponding author

Correspondence to Yusen He.

Additional information

Recommended by Editor Chongdu Cho

Yusen He received his M.Sc. degree in Statistics from the University of Iowa, United States in 2013. He is currently persuing his Ph.D. degree in Industrial Engineering from the University of Iowa. His main research interests include application of data-mining on machinery failures and energy power systems.

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He, Y., Kusiak, A., Ouyang, T. et al. Data-driven modeling of truck engine exhaust valve failures: A case study. J Mech Sci Technol 31, 2747–2757 (2017). https://doi.org/10.1007/s12206-017-0518-1

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  • DOI: https://doi.org/10.1007/s12206-017-0518-1

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