Machine and component residual life estimation through the application of neural networks
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DOI: 10.1016/j.ress.2008.05.008
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References listed on IDEAS
- Pham, Hoang & Wang, Hongzhou, 1996. "Imperfect maintenance," European Journal of Operational Research, Elsevier, vol. 94(3), pages 425-438, November.
- Wang, Hongzhou, 2002. "A survey of maintenance policies of deteriorating systems," European Journal of Operational Research, Elsevier, vol. 139(3), pages 469-489, June.
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Cited by:
- Gómez, M.J. & Castejón, C. & GarcÃa-Prada, J.C., 2016. "Automatic condition monitoring system for crack detection in rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 239-247.
- Jain, Amit Kumar & Lad, Bhupesh Kumar, 2020. "Prognosticating RULs while exploiting the future characteristics of operating profiles," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
- Ilgin, Mehmet Ali & Gupta, Surendra M., 2011. "Performance improvement potential of sensor embedded products in environmental supply chains," Resources, Conservation & Recycling, Elsevier, vol. 55(6), pages 580-592.
- Ondemir, Onder & Gupta, Surendra M., 2014. "A multi-criteria decision making model for advanced repair-to-order and disassembly-to-order system," European Journal of Operational Research, Elsevier, vol. 233(2), pages 408-419.
- Ammar Y. Alqahtani & Surendra M. Gupta, 2017. "One-Dimensional Renewable Warranty Management within Sustainable Supply Chain," Resources, MDPI, vol. 6(2), pages 1-26, April.
- Fang, Xiaolei & Zhou, Rensheng & Gebraeel, Nagi, 2015. "An adaptive functional regression-based prognostic model for applications with missing data," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 266-274.
- Wang, Zhaoqiang & Hu, Changhua & Wang, Wenbin & Zhou, Zhijie & Si, Xiaosheng, 2014. "A case study of remaining storage life prediction using stochastic filtering with the influence of condition monitoring," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 186-195.
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Keywords
Neural networks; Condition-monitoring data; Residual life;All these keywords.
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