IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v213y2021ics095183202100209x.html
   My bibliography  Save this article

A Weibull-based recurrent regression model for repairable systems considering double effects of operation and maintenance: A case study of machine tools

Author

Listed:
  • Hu, Wei
  • Yang, Zhaojun
  • Chen, Chuanhai
  • Wu, Yue
  • Xie, Qunya

Abstract

The failure processes of repairable systems may be impacted by operational conditions and repair activities. In reliability engineering, many real-life data have a bathtub-shaped failure intensity (FI). In order to handle the relationship between the effects of operational conditions and maintenance and bathtub-shaped FI, this paper proposes a double effects regression model in the context of the Weibull-based general renewal process. The model considers the multiplicative and cumulative effects of operational conditions, indicating both scale and shape parameters of the underlying Weibull distribution can potentially change with operational covariates and virtual age. It extends a recurrent regression model to the case of a bathtub-shaped FI and further provides more flexible FI shapes by setting different ranges of effect coefficients. An efficient parameter estimation procedure is illustrated to support the application of the proposed model. The proposed model is shown to more closely describe the bathtub-shaped failure process through several simulated and real-life examples.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s095183202100209x
    DOI: 10.1016/j.ress.2021.107669
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095183202100209X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2021.107669?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tanwar, Monika & Rai, Rajiv N. & Bolia, Nomesh, 2014. "Imperfect repair modeling using Kijima type generalized renewal process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 24-31.
    2. Peng, Weiwen & Shen, Lijuan & Shen, Yan & Sun, Qiuzhuang, 2018. "Reliability analysis of repairable systems with recurrent misuse-induced failures and normal-operation failures," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 87-98.
    3. Brenière, Léa & Doyen, Laurent & Bérenguer, Christophe, 2020. "Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    4. Amin Moniri-Morad & Mohammad Pourgol-Mohammad & Hamid Aghababaei & Javad Sattarvand, 2019. "Reliability-based covariate analysis for complex systems in heterogeneous environment: Case study of mining equipment," Journal of Risk and Reliability, , vol. 233(4), pages 593-604, August.
    5. de Oliveira, Cícero Carlos Felix & Firmino, Paulo Renato Alves & Cristino, Cláudio Tadeu, 2019. "A tool for evaluating repairable systems based on Generalized Renewal Processes," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 281-297.
    6. Joachim Hartung & Dogan Argaç & Kepher Makambi, 2002. "Small sample properties of tests on homogeneity in one—way Anova and Meta—analysis," Statistical Papers, Springer, vol. 43(2), pages 197-235, April.
    7. 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.
    8. Hongzhou Li & Zhaojun Yang & Binbin Xu & Chuanhai Chen & Yingnan Kan & Guofei Liu, 2016. "Reliability Evaluation of NC Machine Tools considering Working Conditions," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-11, May.
    9. Slimacek, Vaclav & Lindqvist, Bo Henry, 2017. "Nonhomogeneous Poisson process with nonparametric frailty and covariates," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 75-83.
    10. Ricardo José Ferreira & Paulo Renato Alves Firmino & Cláudio Tadeu Cristino, 2015. "A Mixed Kijima Model Using the Weibull-Based Generalized Renewal Processes," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-17, July.
    11. Izquierdo, J. & Crespo Márquez, A. & Uribetxebarria, J., 2019. "Dynamic artificial neural network-based reliability considering operational context of assets," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 483-493.
    12. Yevkin, Alexander & Krivtsov, Vasiliy, 2020. "A generalized model for recurrent failures prediction," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    13. Ofer Harel, 2009. "The estimation of R2 and adjusted R2 in incomplete data sets using multiple imputation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(10), pages 1109-1118.
    14. Dijoux, Yann, 2009. "A virtual age model based on a bathtub shaped initial intensity," Reliability Engineering and System Safety, Elsevier, vol. 94(5), pages 982-989.
    15. Krivtsov, V. & Yevkin, O., 2013. "Estimation of G-renewal process parameters as an ill-posed inverse problem," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 10-18.
    16. Thijssens, O.W.M. & Verhagen, Wim J.C., 2020. "Application of Extended Cox Regression Model to Time-On-Wing Data of Aircraft Repairables," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    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. 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).
    2. Zhou, Hang & Lopes Genez, Thiago Augusto & Brintrup, Alexandra & Parlikad, Ajith Kumar, 2022. "A hybrid-learning decomposition algorithm for competing risk identification within fleets of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. Abba, Badamasi & Wang, Hong & Bakouch, Hassan S., 2022. "A reliability and survival model for one and two failure modes system with applications to complete and censored datasets," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

    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. 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).
    2. Jiang, Renyan & Li, Fengping & Xue, Wei & Cao, Yu & Zhang, Kunpeng, 2023. "A robust mean cumulative function estimator and its application to overhaul time optimization for a fleet of heterogeneous repairable systems," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    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. Tanwar, Monika & Rai, Rajiv N. & Bolia, Nomesh, 2014. "Imperfect repair modeling using Kijima type generalized renewal process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 24-31.
    5. 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.
    6. Serguei Maximov & Consuelo de J. Cortes-Penagos, 2020. "A long-time asymptotic solution to the g-renewal equation for underlying distributions with nondecreasing hazard functions," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 92(2), pages 311-341, October.
    7. Wu, Shaomin, 2021. "Two methods to approximate the superposition of imperfect failure processes," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    8. 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.
    9. 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).
    10. Almeida, Marco Pollo & Paixão, Rafael S. & Ramos, Pedro L. & Tomazella, Vera & Louzada, Francisco & Ehlers, Ricardo S., 2020. "Bayesian non-parametric frailty model for dependent competing risks in a repairable systems framework," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    11. Yang, Duo & He, Zhen & He, Shuguang, 2016. "Warranty claims forecasting based on a general imperfect repair model considering usage rate," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 147-154.
    12. 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.
    13. Dewan, Isha & Dijoux, Yann, 2015. "Modelling repairable systems with an early life under competing risks and asymmetric virtual age," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 215-224.
    14. Shane S. Dikolli & John C. Heater & William J. Mayew & Mani Sethuraman, 2021. "Chief Financial Officer Co-option and Chief Executive Officer Compensation," Management Science, INFORMS, vol. 67(3), pages 1939-1955, March.
    15. Wang, Rongxi & Li, Yufan & Xu, Jinjin & Wang, Zhen & Gao, Jianmin, 2022. "F2G: A hybrid fault-function graphical model for reliability analysis of complex equipment with coupled faults," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    16. 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.
    17. Maxim Finkelstein & Ji Hwan Cha, 2022. "Reducing degradation and age of items in imperfect repair modeling," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1058-1081, December.
    18. Xin-Yu Tian & Xincheng Shi & Cheng Peng & Xiao-Jian Yi, 2021. "A Reliability Growth Process Model with Time-Varying Covariates and Its Application," Mathematics, MDPI, vol. 9(8), pages 1-15, April.
    19. Kim, Myeonghyeon & Bae, Jiheon, 2021. "Modeling the flight departure delay using survival analysis in South Korea," Journal of Air Transport Management, Elsevier, vol. 91(C).
    20. Mario Hasler, 2016. "Heteroscedasticity: multiple degrees of freedom vs. sandwich estimation," Statistical Papers, Springer, vol. 57(1), pages 55-68, March.

    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:eee:reensy:v:213:y:2021:i:c:s095183202100209x. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.