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Infinite-Horizon Degradation Control Based on Optimization of Degradation-Aware Cost Function

Author

Listed:
  • Amirhossein Hosseinzadeh Dadash

    (Department of Electronics, Mathematics and Sciences, University of Gävle, 80176 Gävle, Sweden)

  • Niclas Björsell

    (Department of Electronics, Mathematics and Sciences, University of Gävle, 80176 Gävle, Sweden)

Abstract

Controlling machine degradation enhances the accuracy of the remaining-useful-life estimation and offers the ability to control failure type and time. In order to achieve optimal degradation control, the system controller must be cognizant of the consequences of its actions by considering the degradation each action imposes on the system. This article presents a method for designing cost-aware controllers for linear systems, to increase system reliability and availability through degradation control. The proposed framework enables learning independent of the system’s physical structure and working conditions, enabling controllers to choose actions that reduce system degradation while increasing system lifetime. To this end, the cost of each controller’s action is calculated based on its effect on the state of health. A mathematical structure is proposed, to incorporate these costs into the cost function of the linear–quadratic controller, allowing for optimal feedback for degradation control. A simulation validates the proposed method, demonstrating that the optimal-control method based on the proposed cost function outperforms the linear–quadratic regulator in several ways.

Suggested Citation

  • Amirhossein Hosseinzadeh Dadash & Niclas Björsell, 2024. "Infinite-Horizon Degradation Control Based on Optimization of Degradation-Aware Cost Function," Mathematics, MDPI, vol. 12(5), pages 1-24, February.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:729-:d:1348784
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    References listed on IDEAS

    as
    1. Bin Liu & Min Xie & Way Kuo, 2016. "Reliability modeling and preventive maintenance of load-sharing systemswith degrading components," IISE Transactions, Taylor & Francis Journals, vol. 48(8), pages 699-709, August.
    2. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    3. Jeong, Haedong & Park, Bumsoo & Park, Seungtae & Min, Hyungcheol & Lee, Seungchul, 2019. "Fault detection and identification method using observer-based residuals," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 27-40.
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