IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v137y2015icp120-128.html
   My bibliography  Save this article

Computing confidence and prediction intervals of industrial equipment degradation by bootstrapped support vector regression

Author

Listed:
  • Lins, Isis Didier
  • Droguett, Enrique López
  • Moura, Márcio das Chagas
  • Zio, Enrico
  • Jacinto, Carlos Magno

Abstract

Data-driven learning methods for predicting the evolution of the degradation processes affecting equipment are becoming increasingly attractive in reliability and prognostics applications. Among these, we consider here Support Vector Regression (SVR), which has provided promising results in various applications. Nevertheless, the predictions provided by SVR are point estimates whereas in order to take better informed decisions, an uncertainty assessment should be also carried out. For this, we apply bootstrap to SVR so as to obtain confidence and prediction intervals, without having to make any assumption about probability distributions and with good performance even when only a small data set is available. The bootstrapped SVR is first verified on Monte Carlo experiments and then is applied to a real case study concerning the prediction of degradation of a component from the offshore oil industry. The results obtained indicate that the bootstrapped SVR is a promising tool for providing reliable point and interval estimates, which can inform maintenance-related decisions on degrading components.

Suggested Citation

  • Lins, Isis Didier & Droguett, Enrique López & Moura, Márcio das Chagas & Zio, Enrico & Jacinto, Carlos Magno, 2015. "Computing confidence and prediction intervals of industrial equipment degradation by bootstrapped support vector regression," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 120-128.
    • Handle: RePEc:eee:reensy:v:137:y:2015:i:c:p:120-128
    DOI: 10.1016/j.ress.2015.01.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832015000174
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2015.01.007?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. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    2. Cribari-Neto, Francisco, 2004. "Asymptotic inference under heteroskedasticity of unknown form," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 215-233, March.
    3. Davidson, James & Monticini, Andrea & Peel, David, 2007. "Implementing the wild bootstrap using a two-point distribution," Economics Letters, Elsevier, vol. 96(3), pages 309-315, September.
    4. Moura, Márcio das Chagas & Zio, Enrico & Lins, Isis Didier & Droguett, Enrique, 2011. "Failure and reliability prediction by support vector machines regression of time series data," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1527-1534.
    5. Lins, Isis Didier & Araujo, Moacyr & Moura, Márcio das Chagas & Silva, Marcus André & Droguett, Enrique López, 2013. "Prediction of sea surface temperature in the tropical Atlantic by support vector machines," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 187-198.
    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. Yang, Chunzhen & Liu, Jingquan & Zeng, Yuyun & Xie, Guangyao, 2019. "Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model," Renewable Energy, Elsevier, vol. 133(C), pages 433-441.
    2. Arias-Rosales, Andrés & Osorio-Gómez, Gilberto, 2018. "Wind turbine selection method based on the statistical analysis of nominal specifications for estimating the cost of energy," Applied Energy, Elsevier, vol. 228(C), pages 980-998.
    3. Pan, Yue & Qin, Jianjun & Hou, Yongmao & Chen, Jin-Jian, 2024. "Two-stage support vector machine-enabled deep excavation settlement prediction considering class imbalance and multi-source uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Negreiros, Ana Cláudia Souza Vidal de & Lins, Isis Didier & Moura, Márcio José das Chagas & Droguett, Enrique López, 2020. "Reliability data analysis of systems in the wear-out phase using a (corrected) q-Exponential likelihood," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    5. Feng, Liuyang & Zhang, Limao, 2022. "Enhanced prediction intervals of tunnel-induced settlement using the genetic algorithm and neural network," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    6. Yongtong Shao & Tao Xiong & Minghao Li & Dermot Hayes & Wendong Zhang & Wei Xie, 2021. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1082-1098, May.
    7. Xu, Yuan & Zhang, Mingqing & Ye, Liangliang & Zhu, Qunxiong & Geng, Zhiqiang & He, Yan-Lin & Han, Yongming, 2018. "A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction," Energy, Elsevier, vol. 164(C), pages 137-146.

    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. Wu, Xuedong & Chang, Yanchao & Mao, Jianxu & Du, Zhaoping, 2013. "Predicting reliability and failures of engine systems by single multiplicative neuron model with iterated nonlinear filters," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 244-250.
    2. Wei, Zhao & Tao, Tao & ZhuoShu, Ding & Zio, Enrico, 2013. "A dynamic particle filter-support vector regression method for reliability prediction," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 109-116.
    3. 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).
    4. Yongquan, Sun & Xi, Chen & He, Ren & Yingchao, Jin & Quanwu, Liu, 2016. "Ordering decision-making methods on spare parts for a new aircraft fleet based on a two-sample prediction," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 40-50.
    5. Benoît Le Maux & Federica Minardy & Charlotte Magalhaes, 2011. "Determinants of Electoral Outcomes: A simple Test of Meltzer and Richard's Hypothesis," Economics Working Paper from Condorcet Center for political Economy at CREM-CNRS 2011-03-ccr, Condorcet Center for political Economy.
    6. 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.
    7. 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.
    8. Gao, Shuzhi & Zhang, Sixuan & Zhang, Yimin & Gao, Yue, 2020. "Operational reliability evaluation and prediction of rolling bearing based on isometric mapping and NoCuSa-LSSVM," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    9. Donald W. K. Andrews & Patrik Guggenberger, 2014. "A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter," The Review of Economics and Statistics, MIT Press, vol. 96(2), pages 376-381, May.
    10. repec:ags:aaea22:335733 is not listed on IDEAS
    11. David Roodman & James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2019. "Fast and wild: Bootstrap inference in Stata using boottest," Stata Journal, StataCorp LP, vol. 19(1), pages 4-60, March.
    12. Roy, Atin & Chakraborty, Subrata, 2022. "Reliability analysis of structures by a three-stage sequential sampling based adaptive support vector regression model," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    13. Szafranek, Karol, 2017. "Flattening of the New Keynesian Phillips curve: Evidence for an emerging, small open economy," Economic Modelling, Elsevier, vol. 63(C), pages 334-348.
    14. Katarzyna Jabłońska, 2018. "Dealing With Heteroskedasticity Within The Modeling Of The Quality Of Life Of Older People," Statistics in Transition New Series, Polish Statistical Association, vol. 19(3), pages 423-452, September.
    15. Rodrigo Andrade & Somayeh Moazeni & Jose Emmanuel Ramirez‐Marquez, 2020. "A systems perspective on contact centers and customer service reliability modeling," Systems Engineering, John Wiley & Sons, vol. 23(2), pages 221-236, March.
    16. Phan, Hieu Chi & Dhar, Ashutosh Sutra & Bui, Nang Duc, 2023. "Reliability assessment of pipelines crossing strike-slip faults considering modeling uncertainties using ANN models," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    17. Rocchetta, Roberto & Crespo, Luis G., 2021. "A scenario optimization approach to reliability-based and risk-based design: Soft-constrained modulation of failure probability bounds," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    18. Cheng, Tsung-Chi, 2012. "On simultaneously identifying outliers and heteroscedasticity without specific form," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2258-2272.
    19. Pötscher, Benedikt M. & Preinerstorfer, David, 2021. "Valid Heteroskedasticity Robust Testing," MPRA Paper 107420, University Library of Munich, Germany.
    20. Niu, Gang & Yang, Bo-Suk & Pecht, Michael, 2010. "Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 786-796.
    21. Teng, Kuei-Yung & Thekdi, Shital A. & Lambert, James H., 2012. "Identification and evaluation of priorities in the business process of a risk or safety organization," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 74-86.

    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:137:y:2015:i:c:p:120-128. 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.