Inspection schedule for prognostics with uncertainty management
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DOI: 10.1016/j.ress.2022.108391
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Cited by:
- Esposito, Nicola & Mele, Agostino & Castanier, Bruno & GIORGIO, Massimiliano, 2023. "A hybrid maintenance policy for a deteriorating unit in the presence of three forms of variability," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
- Lee, Juseong & Mitici, Mihaela, 2023. "Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
- Aizpurua, J.I. & Stewart, B.G. & McArthur, S.D.J. & Penalba, M. & Barrenetxea, M. & Muxika, E. & Ringwood, J.V., 2022. "Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
- Kamariotis, Antonios & Tatsis, Konstantinos & Chatzi, Eleni & Goebel, Kai & Straub, Daniel, 2024. "A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
- Fan, Xudong & Zhang, Xijin & Yu, Xiong Bill, 2023. "Uncertainty quantification of a deep learning model for failure rate prediction of water distribution networks," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
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Keywords
Uncertainty reduction; Prognostics; Data measurement; Inspection schedule; Epistemic; Aleatory;All these keywords.
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