IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v227y2013i1p55-65.html
   My bibliography  Save this article

Log-linear process modeling for repairable systems with time trends and its applications in reliability assessment of numerically controlled machine tools

Author

Listed:
  • Zhi-Ming Wang
  • Xia Yu

Abstract

Two non-homogeneous Poisson processes including the power law process and the log-linear process with reliability improvement or deterioration are analyzed. Based on Akaike information criterion and Bayesian information criterion, the best model of failure data is presented. The point maximum likelihood and interval estimators of the parameters, as well as seven reliability indices of the log-linear process model, such as cumulative mean time between failures, cumulative number of failures, reliability at a given time, and warranty time given reliability are given. In tests for failure time trends, both the graphical methods, including the cumulative failures versus time plot and the total-time-on-test plot, and the analytical methods including the Laplace, the Military Handbook, and the Lewis–Robinson tests are used. Three real cases for failure data with failure truncation and time truncation of multiple numerically controlled machine tools are given to illustrate the use of the proposed models.

Suggested Citation

  • Zhi-Ming Wang & Xia Yu, 2013. "Log-linear process modeling for repairable systems with time trends and its applications in reliability assessment of numerically controlled machine tools," Journal of Risk and Reliability, , vol. 227(1), pages 55-65, February.
    • Handle: RePEc:sae:risrel:v:227:y:2013:i:1:p:55-65
    DOI: 10.1177/1748006X12460633
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X12460633
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X12460633?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. Louit, D.M. & Pascual, R. & Jardine, A.K.S., 2009. "A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1618-1628.
    2. Viertävä, Janne & Vaurio, Jussi K., 2009. "Testing statistical significance of trends in learning, ageing and safety indicators," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1128-1132.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Van Dyck, Jozef & Verdonck, Tim, 2014. "Precision of power-law NHPP estimates for multiple systems with known failure rate scaling," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 143-152.

    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. Rajkumar Bhimgonda Patil & Basavraj S Kothavale & Laxman Yadu Waghmode, 2019. "Selection of time-to-failure model for computerized numerical control turning center based on the assessment of trends in maintenance data," Journal of Risk and Reliability, , vol. 233(2), pages 105-117, April.
    2. Garmabaki, A.H.S. & Ahmadi, Alireza & Block, Jan & Pham, Hoang & Kumar, Uday, 2016. "A reliability decision framework for multiple repairable units," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 78-88.
    3. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    4. Braglia, Marcello & Carmignani, Gionata & Frosolini, Marco & Zammori, Francesco, 2012. "Data classification and MTBF prediction with a multivariate analysis approach," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 27-35.
    5. Louit, D.M. & Pascual, R. & Jardine, A.K.S., 2009. "A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1618-1628.
    6. Barabadi, A. & Ayele, Y.Z., 2018. "Post-disaster infrastructure recovery: Prediction of recovery rate using historical data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 209-223.
    7. Hu, Wei & Yang, Zhaojun & Chen, Chuanhai & Wu, Yue & Xie, Qunya, 2021. "A Weibull-based recurrent regression model for repairable systems considering double effects of operation and maintenance: A case study of machine tools," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    8. Hu, Wei & Westerlund, Per & Hilber, Patrik & Chen, Chuanhai & Yang, Zhaojun, 2022. "A general model, estimation, and procedure for modeling recurrent failure process of high-voltage circuit breakers considering multivariate impacts," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    9. Peng, Yizhen & Wang, Yu & Zi, YanYang & Tsui, Kwok-Leung & Zhang, Chuhua, 2017. "Dynamic reliability assessment and prediction for repairable systems with interval-censored data," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 301-309.
    10. Syamsundar, A. & Naikan, V.N.A. & Wu, Shaomin, 2021. "Extended Arithmetic Reduction of Age Models for the Failure Process of a Repairable System," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    11. Ali Nouri Gharahasanlou & Mohammad Ataei & Reza Khalokakaie & Abbas Barabadi & Vahid Einian, 2017. "Risk based maintenance strategy: a quantitative approach based on time-to-failure model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(3), pages 602-611, September.
    12. Juan Izquierdo & Adolfo Crespo Márquez & Jone Uribetxebarria & Asier Erguido, 2019. "Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance," Energies, MDPI, vol. 12(11), pages 1-17, May.
    13. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico & Ault, Graham & Bell, Keith, 2013. "Reinforcement learning for microgrid energy management," Energy, Elsevier, vol. 59(C), pages 133-146.
    14. Wu, Shaomin, 2021. "Two methods to approximate the superposition of imperfect failure processes," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    15. Mostafa Aliyari & Yonas Z Ayele & Abbas Barabadi & Enrique Lopez Droguett, 2019. "Risk analysis in low-voltage distribution systems," Journal of Risk and Reliability, , vol. 233(2), pages 118-138, April.
    16. Hamzeh Soltanali & A.H.S Garmabaki & Adithya Thaduri & Aditya Parida & Uday Kumar & Abbas Rohani, 2019. "Sustainable production process: An application of reliability, availability, and maintainability methodologies in automotive manufacturing," Journal of Risk and Reliability, , vol. 233(4), pages 682-697, August.
    17. Rezgar Zaki & Abbas Barabadi & Javad Barabady & Ali Nouri Qarahasanlou, 2022. "Observed and unobserved heterogeneity in failure data analysis," Journal of Risk and Reliability, , vol. 236(1), pages 194-207, February.
    18. Seyed Hadi Hoseinie & Hussan Al-Chalabi & Behzad Ghodrati, 2018. "Comparison between Simulation and Analytical Methods in Reliability Data Analysis: A Case Study on Face Drilling Rigs," Data, MDPI, vol. 3(2), pages 1-12, April.
    19. Vanderschueren, Toon & Boute, Robert & Verdonck, Tim & Baesens, Bart & Verbeke, Wouter, 2023. "Optimizing the preventive maintenance frequency with causal machine learning," International Journal of Production Economics, Elsevier, vol. 258(C).
    20. Moghadam, Mehdi Akbari & Bagheri, Sajad & Salemi, Amir Hosein & Tavakoli, Mohammad Bagher, 2023. "Long-term maintenance planning of medium voltage overhead lines considering the uncertainties and reasons for interruption in a real distribution network," Reliability Engineering and System Safety, Elsevier, vol. 233(C).

    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:sae:risrel:v:227:y:2013:i:1:p:55-65. 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: SAGE Publications (email available below). General contact details of provider: .

    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.