IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v233y2019i2p105-117.html
   My bibliography  Save this article

Selection of time-to-failure model for computerized numerical control turning center based on the assessment of trends in maintenance data

Author

Listed:
  • Rajkumar Bhimgonda Patil
  • Basavraj S Kothavale
  • Laxman Yadu Waghmode

Abstract

This article provides a generalized framework for selection of time-to-failure model based on the assessment of trends in failure and repair time data. This framework is based on modifications of existing frameworks and can be applied for binary as well as multi-state systems. The proposed framework is applied for reliability analysis of a computerized numerical control turning center. For analysis purpose, the failure data are collected for 50 computerized numerical control turning center over a period of 7 years for three different working conditions, that is, when machining material is steel, aluminum and cast iron. The data collected are then processed using the proposed framework and the best-fit distribution is found for the time-to-failure data. Furthermore, the reliable life and reliabilities of the different sub-systems are estimated. From the analysis, it is found that spindle system, computerized numerical control system, electrical and electronic system, hydraulic system and cooling system are found to be critical from reliability and maintainability point of view. The analysis presented here is expected to help the users and manufacturers of computerized numerical control turning center to estimate the reliability in accurate manner.

Suggested Citation

  • 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.
  • Handle: RePEc:sae:risrel:v:233:y:2019:i:2:p:105-117
    DOI: 10.1177/1748006X18759124
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X18759124
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1748006X18759124?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. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    2. Castet, Jean-Francois & Saleh, Joseph H., 2010. "Beyond reliability, multi-state failure analysis of satellite subsystems: A statistical approach," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 311-322.
    3. Percy, David F. & Kobbacy, Khairy A. H. & Fawzi, Bahir B., 1997. "Setting preventive maintenance schedules when data are sparse," International Journal of Production Economics, Elsevier, vol. 51(3), pages 223-234, September.
    4. Barabady, Javad & Kumar, Uday, 2008. "Reliability analysis of mining equipment: A case study of a crushing plant at Jajarm Bauxite Mine in Iran," Reliability Engineering and System Safety, Elsevier, vol. 93(4), pages 647-653.
    5. Scarf, Philip A., 1997. "On the application of mathematical models in maintenance," European Journal of Operational Research, Elsevier, vol. 99(3), pages 493-506, June.
    6. Bhupesh Kumar Lad & Makarand S. Kulkarni, 2010. "A parameter estimation method for machine tool reliability analysis using expert judgement," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 2(2), pages 155-169.
    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. Kim, Jin Seon & Yum, Bong-Jin, 2008. "Selection between Weibull and lognormal distributions: A comparative simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 477-485, December.
    9. Bobrowski, Sebastian & Chen, Hong & Döring, Maik & Jensen, Uwe & Schinköthe, Wolfgang, 2015. "Estimation of the lifetime distribution of mechatronic systems in the presence of a covariate: A comparison among parametric, semiparametric and nonparametric models," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 105-112.
    10. 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.
    11. Lisnianski, Anatoly & Ding, Yi, 2009. "Redundancy analysis for repairable multi-state system by using combined stochastic processes methods and universal generating function technique," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1788-1795.
    12. Barabadi, Abbas & Barabady, Javad & Markeset, Tore, 2014. "Application of reliability models with covariates in spare part prediction and optimization – A case study," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 1-7.
    13. Regattieri, A. & Manzini, R. & Battini, D., 2010. "Estimating reliability characteristics in the presence of censored data: A case study in a light commercial vehicle manufacturing system," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1093-1102.
    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. 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. 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. & 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.
    4. 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.
    5. Percy, David F. & Kobbacy, Khairy A. H., 2000. "Determining economical maintenance intervals," International Journal of Production Economics, Elsevier, vol. 67(1), pages 87-94, August.
    6. Yun Zhang & Zhengguo Xu & Xinli Wang & Jiangang Lu & Youxian Sun, 2014. "Single minimal path based backup path for multi-state network," Journal of Risk and Reliability, , vol. 228(2), pages 152-165, April.
    7. 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.
    8. 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.
    9. 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.
    10. Percy, David F., 2002. "Bayesian enhanced strategic decision making for reliability," European Journal of Operational Research, Elsevier, vol. 139(1), pages 133-145, May.
    11. Masoud Naseri & Javad Barabady, 2016. "On RAM performance of production facilities operating under the Barents Sea harsh environmental conditions," 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. 7(3), pages 273-298, September.
    12. Ali N Qarahasanlou & Abbas Barabadi & Yonas Z Ayele, 2018. "Production performance analysis during operation phase: A case study," Journal of Risk and Reliability, , vol. 232(6), pages 559-575, December.
    13. 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.
    14. Taghipour, Sharareh & Banjevic, Dragan, 2011. "Trend analysis of the power law process using Expectation–Maximization algorithm for data censored by inspection intervals," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1340-1348.
    15. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    16. Regattieri, A. & Manzini, R. & Battini, D., 2010. "Estimating reliability characteristics in the presence of censored data: A case study in a light commercial vehicle manufacturing system," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1093-1102.
    17. Jafary, Bentolhoda & Fiondella, Lance, 2016. "A universal generating function-based multi-state system performance model subject to correlated failures," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 16-27.
    18. Asadzadeh, S.M. & Azadeh, A., 2014. "An integrated systemic model for optimization of condition-based maintenance with human error," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 117-131.
    19. Peters, Lennart & Madlener, Reinhard, 2017. "Economic evaluation of maintenance strategies for ground-mounted solar photovoltaic plants," Applied Energy, Elsevier, vol. 199(C), pages 264-280.
    20. Kuschel, Torben & Bock, Stefan, 2016. "The weighted uncapacitated planned maintenance problem: Complexity and polyhedral properties," European Journal of Operational Research, Elsevier, vol. 250(3), pages 773-781.

    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:233:y:2019:i:2:p:105-117. 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.