A state space condition monitoring model for furnace erosion prediction and replacement

Citations

Cited by:

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2. Si, Xiao-Sheng & Chen, Mao-Yin & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2013. "Specifying measurement errors for required lifetime estimation performance," European Journal of Operational Research, Elsevier, vol. 231(3), pages 631-644.
3. Mitra Fouladirad & Antoine Grall, 2015. "Monitoring and condition-based maintenance with abrupt change in a system’s deterioration rate," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(12), pages 2183-2194, September.
4. Pedregal, Diego J. & Carmen Carnero, Ma., 2009. "Vibration analysis diagnostics by continuous-time models: A case study," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 244-253.
5. Nishit Kumar Srivastava & Sandeep Mondal, 2016. "Development of Predictive Maintenance Model for N-Component Repairable System Using NHPP Models and System Availability Concept," Global Business Review, International Management Institute, vol. 17(1), pages 105-115, February.
6. Diego Pedregal & Fausto García & Clive Roberts, 2009. "An algorithmic approach for maintenance management based on advanced state space systems and harmonic regressions," Annals of Operations Research, Springer, vol. 166(1), pages 109-124, February.
7. Wu, Jianmou & Makis, Viliam, 2008. "Economic and economic-statistical design of a chi-square chart for CBM," European Journal of Operational Research, Elsevier, vol. 188(2), pages 516-529, July.
8. García Márquez, Fausto Pedro & Schmid, Felix, 2007. "A digital filter-based approach to the remote condition monitoring of railway turnouts," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 830-840.
9. E. Skordilis & R. Moghaddass, 2017. "A condition monitoring approach for real-time monitoring of degrading systems using Kalman filter and logistic regression," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5579-5596, October.
10. Carnero Moya, M. Carmen, 2004. "The control of the setting up of a predictive maintenance programme using a system of indicators," Omega, Elsevier, vol. 32(1), pages 57-75, February.
11. de Jonge, Bram & Teunter, Ruud & Tinga, Tiedo, 2017. "The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 21-30.
12. Zhengxin Zhang & Xiaosheng Si & Changhua Hu & Xiangyu Kong, 2015. "Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity," Journal of Risk and Reliability, , vol. 229(4), pages 343-355, August.
13. Cadini, F. & Zio, E. & Avram, D., 2009. "Model-based Monte Carlo state estimation for condition-based component replacement," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 752-758.
14. Carr, Matthew J. & Wang, Wenbin, 2011. "An approximate algorithm for prognostic modelling using condition monitoring information," European Journal of Operational Research, Elsevier, vol. 211(1), pages 90-96, May.
15. Bana e Costa, Carlos A. & Carnero, María Carmen & Oliveira, Mónica Duarte, 2012. "A multi-criteria model for auditing a Predictive Maintenance Programme," European Journal of Operational Research, Elsevier, vol. 217(2), pages 381-393.
16. Carnero, MaCarmen, 2006. "An evaluation system of the setting up of predictive maintenance programmes," Reliability Engineering and System Safety, Elsevier, vol. 91(8), pages 945-963.
17. Pedregal, Diego J. & Carmen Carnero, Ma, 2006. "State space models for condition monitoring: a case study," Reliability Engineering and System Safety, Elsevier, vol. 91(2), pages 171-180.
18. Daming Lin & Viliam Makis, 2006. "On‐line parameter estimation for a partially observable system subject to random failure," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(5), pages 477-483, August.
19. García, Fausto P. & Pedregal, Diego J. & Roberts, Clive, 2010. "Time series methods applied to failure prediction and detection," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 698-703.
20. W Wang, 2011. "Overview of a semi-stochastic filtering approach for residual life estimation with applications in condition based maintenance," Journal of Risk and Reliability, , vol. 225(2), pages 185-197, June.
21. Rui Jiang & Michael Kim & Viliam Makis, 2012. "A Bayesian model and numerical algorithm for CBM availability maximization," Annals of Operations Research, Springer, vol. 196(1), pages 333-348, July.
22. 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.
23. Fausto Pedro García Márquez & Diego J. Pedregal & Clive Roberts, 2015. "New methods for the condition monitoring of level crossings," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(5), pages 878-884, April.
24. Nguyen, Khanh T.P. & Medjaher, Kamal, 2019. "A new dynamic predictive maintenance framework using deep learning for failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 251-262.
25. Ming-Yi You & Guang Meng, 2012. "A modularized framework for predictive maintenance scheduling," Journal of Risk and Reliability, , vol. 226(4), pages 380-391, August.
26. Yawei Hu & Shujie Liu & Huitian Lu & Hongchao Zhang, 2018. "Online remaining useful life prognostics using an integrated particle filter," Journal of Risk and Reliability, , vol. 232(6), pages 587-597, December.
27. Makis, Viliam & Wu, Jianmou & Gao, Yan, 2006. "An application of DPCA to oil data for CBM modeling," European Journal of Operational Research, Elsevier, vol. 174(1), pages 112-123, October.