IDEAS home Printed from https://ideas.repec.org/a/wly/navres/v53y2006i5p477-483.html
   My bibliography  Save this article

On‐line parameter estimation for a partially observable system subject to random failure

Author

Listed:
  • Daming Lin
  • Viliam Makis

Abstract

In this paper, we study the on‐line parameter estimation problem for a partially observable system subject to deterioration and random failure. The state of the system evolves according to a continuous time homogeneous Markov process with a finite state space. The system state is not observable, except for the failure state. The information related to the system state is available at discrete times through inspections. A recursive maximum likelihood (RML) algorithm is proposed for the on‐line parameter estimation of the model. The RML algorithm proposed in the paper is considerably faster and easier to apply than other RML algorithms in the literature, because it does not require projection into the constraint domain and calculation of the gradient on the surface of the constraint manifolds. The algorithm is illustrated by an example using real vibration data. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006

Suggested Citation

  • Daming Lin & Viliam Makis, 2006. "On‐line parameter estimation for a partially observable system subject to random failure," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(5), pages 477-483, August.
    • Handle: RePEc:wly:navres:v:53:y:2006:i:5:p:477-483
    DOI: 10.1002/nav.20156
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/nav.20156
    Download Restriction: no
    File URL: https://libkey.io/10.1002/nav.20156?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wallace J. Hopp & Yar‐Lin Kuo, 1998. "An optimal structured policy for maintenance of partially observable aircraft engine components," Naval Research Logistics (NRL), John Wiley & Sons, vol. 45(4), pages 335-352, June.
    2. V. Makis & X. Jiang, 2003. "Optimal Replacement Under Partial Observations," Mathematics of Operations Research, INFORMS, vol. 28(2), pages 382-394, May.
    3. Stadje, W., 1994. "Maximal wearing-out of a deteriorating system: An optimal stopping approach," European Journal of Operational Research, Elsevier, vol. 73(3), pages 472-479, March.
    4. Christer, A. H. & Wang, W. & Sharp, J. M., 1997. "A state space condition monitoring model for furnace erosion prediction and replacement," European Journal of Operational Research, Elsevier, vol. 101(1), pages 1-14, August.
    5. Uwe Jensen & Guang-Hui Hsu, 1993. "Optimal Stopping by Means of Point Process Observations with Applications in Reliability," Mathematics of Operations Research, INFORMS, vol. 18(3), pages 645-657, August.
    6. Edward J. Sondik, 1978. "The Optimal Control of Partially Observable Markov Processes over the Infinite Horizon: Discounted Costs," Operations Research, INFORMS, vol. 26(2), pages 282-304, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. V. Makis & X. Jiang, 2003. "Optimal Replacement Under Partial Observations," Mathematics of Operations Research, INFORMS, vol. 28(2), pages 382-394, May.
    2. Ming-Yi You & Guang Meng, 2012. "A modularized framework for predictive maintenance scheduling," Journal of Risk and Reliability, , vol. 226(4), pages 380-391, August.
    3. de Jonge, Bram & Teunter, Ruud & Tinga, Tiedo, 2017. "The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 21-30.
    4. Hao Zhang & Weihua Zhang, 2023. "Analytical Solution to a Partially Observable Machine Maintenance Problem with Obvious Failures," Management Science, INFORMS, vol. 69(7), pages 3993-4015, July.
    5. Makis, Viliam & Wu, Jianmou & Gao, Yan, 2006. "An application of DPCA to oil data for CBM modeling," European Journal of Operational Research, Elsevier, vol. 174(1), pages 112-123, October.
    6. Rui Jiang & Michael Kim & Viliam Makis, 2012. "A Bayesian model and numerical algorithm for CBM availability maximization," Annals of Operations Research, Springer, vol. 196(1), pages 333-348, July.
    7. Carr, Matthew J. & Wang, Wenbin, 2011. "An approximate algorithm for prognostic modelling using condition monitoring information," European Journal of Operational Research, Elsevier, vol. 211(1), pages 90-96, May.
    8. Wu, Jianmou & Makis, Viliam, 2008. "Economic and economic-statistical design of a chi-square chart for CBM," European Journal of Operational Research, Elsevier, vol. 188(2), pages 516-529, July.
    9. Reza Ahmadi, 2024. "Reliability and maintenance modeling for a production system by means of point process observations," Annals of Operations Research, Springer, vol. 340(1), pages 3-26, September.
    10. Kim, Michael Jong & Jiang, Rui & Makis, Viliam & Lee, Chi-Guhn, 2011. "Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure," European Journal of Operational Research, Elsevier, vol. 214(2), pages 331-339, October.
    11. E. Skordilis & R. Moghaddass, 2017. "A condition monitoring approach for real-time monitoring of degrading systems using Kalman filter and logistic regression," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5579-5596, October.
    12. Williams, Byron K., 2009. "Markov decision processes in natural resources management: Observability and uncertainty," Ecological Modelling, Elsevier, vol. 220(6), pages 830-840.
    13. Muciek, Bogdan K. & Szajowski, Krzysztof J., 2006. "Optimal Stopping of a Risk Process when Claims are Covered immediately," MPRA Paper 19836, University Library of Munich, Germany, revised 2007.
    14. Zhengxin Zhang & Xiaosheng Si & Changhua Hu & Xiangyu Kong, 2015. "Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity," Journal of Risk and Reliability, , vol. 229(4), pages 343-355, August.
    15. Yanling Chang & Alan Erera & Chelsea White, 2015. "Value of information for a leader–follower partially observed Markov game," Annals of Operations Research, Springer, vol. 235(1), pages 129-153, December.
    16. W Wang, 2011. "Overview of a semi-stochastic filtering approach for residual life estimation with applications in condition based maintenance," Journal of Risk and Reliability, , vol. 225(2), pages 185-197, June.
    17. Michele Compare & Paolo Marelli & Piero Baraldi & Enrico Zio, 2018. "A Markov decision process framework for optimal operation of monitored multi-state systems," Journal of Risk and Reliability, , vol. 232(6), pages 677-689, December.
    18. Seites-Rundlett, William & Bashar, Mohammad Z. & Torres-Machi, Cristina & Corotis, Ross B., 2022. "Combined evidence model to enhance pavement condition prediction from highly uncertain sensor data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    19. Yawei Hu & Shujie Liu & Huitian Lu & Hongchao Zhang, 2018. "Online remaining useful life prognostics using an integrated particle filter," Journal of Risk and Reliability, , vol. 232(6), pages 587-597, December.
    20. Panagiotidou, S. & Tagaras, G., 2012. "Optimal integrated process control and maintenance under general deterioration," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 58-70.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:navres:v:53:y:2006:i:5:p:477-483. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1520-6750 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.