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XGBoost based residual life prediction in the presence of human error in maintenance

  • S.I.: Applications of Machine Learning in Maintenance Engineering and Management (IFAC AMEST 2020)
  • Published:
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Abstract

Accurate maintenance decision making is essential for organizations like military and aviation. Immensely demanding situations like limited time availability for maintenance in strenuous conditions escalate the possibility of human errors in maintaining such equipment. Human errors in maintenance negatively impact the life of the systems. Human Reliability Analysis methodologies have evolved to systematically quantify the human error in terms of Human Error Probability. However, the exact effect of human error on every component’s life is unknown yet. In the presence of the diverse operating profiles for equipment, estimating such effects becomes a complex and mathematically challenging problem to be handled by conventional statistical techniques. This paper presents a machine learning approach to estimate the residual life of a component by incorporating the effect of human error in maintenance. Based on the nature of the maintenance data, a gradient boosting ensemble model (XGBoost) is developed, which predicts the residual life of the component while considering error induced by maintenance personnel during its maintenance. The model recommends the maintenance decision considering the predicted residual life and the user-defined future mission profile. Additionally, provision is made to capture the stochastic future operating profile. The developed model effectively handles the uncertainties and variabilities in expected future mission profiles and the correlation of multiple influencing parameters without increasing mathematical complexity. The developed model is illustrated in the decision making of replacement of a component in a mission-critical military system in pre-mission maintenance break. From the perspective of managerial implications, some of the key findings from numerical experiments on the developed model are presented.

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Acknowledgements

The authors are thankful to the organizing committee of 4th IFAC workshop on Advanced Maintenance Engineering, Services and Technologies (AMEST) 2020, for inviting this article to the topical collection on ‘Applications of Machine Learning in Maintenance Engineering and Management’. Authors also acknowledge the support by the project—IAPP18-19/31 funded by Royal Academy of Engineering, London.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Ram S. Mohril & Bhupesh K. Lad

  2. Department of Mechanical Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Bhupendra S. Solanki & Makarand S. Kulkarni

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  1. Ram S. Mohril
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  2. Bhupendra S. Solanki
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  3. Makarand S. Kulkarni
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  4. Bhupesh K. Lad
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Correspondence to Ram S. Mohril.

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Mohril, R.S., Solanki, B.S., Kulkarni, M.S. et al. XGBoost based residual life prediction in the presence of human error in maintenance. Neural Comput & Applic 35, 3025–3039 (2023). https://doi.org/10.1007/s00521-022-07216-2

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