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Failure and reliability prediction by support vector machines regression of time series data

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Cited by:

  1. Abu-Samah, A. & Shahzad, M.K. & Zamai, E., 2017. "Bayesian based methodology for the extraction and validation of time bound failure signatures for online failure prediction," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 616-628.
  2. 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).
  3. Fang, Chen & Xu, You-Lin & Li, Yongle & Li, Jinrong, 2024. "Serviceability analysis of sea-crossing bridges under correlated wind and wave loads," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
  4. 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.
  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.
  6. 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.
  7. 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).
  8. 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.
  9. Yang Liu & Naiwei Lu & Xinfeng Yin & Mohammad Noori, 2016. "An adaptive support vector regression method for structural system reliability assessment and its application to a cable-stayed bridge," Journal of Risk and Reliability, , vol. 230(2), pages 204-219, April.
  10. 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).
  11. Márcio das Chagas Moura & Enrique López Droguett & Paulo Renato Alves Firmino & Ricardo José Ferreira, 2014. "A competing risk model for dependent and imperfect condition–based preventive and corrective maintenances," Journal of Risk and Reliability, , vol. 228(6), pages 590-605, December.
  12. Yaqun, Qi & Ping, Jin & Ruizhi, Li & Sheng, Zhang & Guobiao, Cai, 2020. "Dynamic reliability analysis for the reusable thrust chamber: A multi-failure modes investigation based on coupled thermal-structural analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
  13. Marseguerra, M., 2014. "Early detection of gradual concept drifts by text categorization and Support Vector Machine techniques: The TRIO algorithm," Reliability Engineering and System Safety, Elsevier, vol. 129(C), pages 1-9.
  14. Oszczypała, Mateusz & Konwerski, Jakub & Ziółkowski, Jarosław & Małachowski, Jerzy, 2024. "Reliability analysis and redundancy optimization of k-out-of-n systems with random variable k using continuous time Markov chain and Monte Carlo simulation," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
  15. Roy, Atin & Chakraborty, Subrata, 2020. "Support vector regression based metamodel by sequential adaptive sampling for reliability analysis of structures," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
  16. Andrés Ruiz-Tagle Palazuelos & Enrique López Droguett & Rodrigo Pascual, 2020. "A novel deep capsule neural network for remaining useful life estimation," Journal of Risk and Reliability, , vol. 234(1), pages 151-167, February.
  17. Colombo, Danilo & Lima, Gilson Brito Alves & Pereira, Danillo Roberto & Papa, João P., 2020. "Regression-based finite element machines for reliability modeling of downhole safety valves," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
  18. Wu, Jianing & Yan, Shaoze & Zuo, Ming J., 2016. "Evaluating the reliability of multi-body mechanisms: A method considering the uncertainties of dynamic performance," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 96-106.
  19. 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.
  20. Utkin, Lev V. & Coolen, Frank P.A., 2018. "A robust weighted SVR-based software reliability growth model," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 93-101.
  21. Roy, Atin & Chakraborty, Subrata, 2023. "Support vector machine in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
  22. Oh, YeongGwang & Ransikarbum, Kasin & Busogi, Moise & Kwon, Daeil & Kim, Namhun, 2019. "Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 202-212.
  23. Rocco S, Claudio M., 2013. "Singular spectrum analysis and forecasting of failure time series," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 126-136.
  24. Li, Zhanhang & Zhou, Jian & Nassif, Hani & Coit, David & Bae, Jinwoo, 2023. "Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
  25. Fink, Olga & Zio, Enrico & Weidmann, Ulrich, 2014. "Predicting component reliability and level of degradation with complex-valued neural networks," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 198-206.
  26. Khatibinia, Mohsen & Javad Fadaee, Mohammad & Salajegheh, Javad & Salajegheh, Eysa, 2013. "Seismic reliability assessment of RC structures including soil–structure interaction using wavelet weighted least squares support vector machine," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 22-33.
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