A new dynamic predictive maintenance framework using deep learning for failure prognostics

Citations

Cited by:

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4. Giovanni Gravito de Carvalho Chrysostomo & Marco Vinicius Bhering de Aguiar Vallim & Leilton Santos da Silva & Leandro A. Silva & Arnaldo Rabello de Aguiar Vallim Filho, 2020. "A Framework for Big Data Analytical Process and Mapping—BAProM: Description of an Application in an Industrial Environment," Energies, MDPI, vol. 13(22), pages 1-28, November.
5. Mitici, Mihaela & de Pater, Ingeborg & Barros, Anne & Zeng, Zhiguo, 2023. "Dynamic predictive maintenance for multiple components using data-driven probabilistic RUL prognostics: The case of turbofan engines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
6. de Pater, Ingeborg & Mitici, Mihaela, 2021. "Predictive maintenance for multi-component systems of repairables with Remaining-Useful-Life prognostics and a limited stock of spare components," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
7. Nguyen, Khanh T.P. & Medjaher, Kamal & Gogu, Christian, 2022. "Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
8. 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).
9. Murray, Brian & Perera, Lokukaluge Prasad, 2021. "An AIS-based deep learning framework for regional ship behavior prediction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
10. Hesabi, Hadis & Nourelfath, Mustapha & Hajji, Adnène, 2022. "A deep learning predictive model for selective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
11. Weili Cai & Wenjuan Zhang & Xiaofeng Hu & Yingchao Liu, 2020. "A hybrid information model based on long short-term memory network for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1497-1510, August.
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14. Hu, Yang & Miao, Xuewen & Si, Yong & Pan, Ershun & Zio, Enrico, 2022. "Prognostics and health management: A review from the perspectives of design, development and decision," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
15. 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).
16. Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine & Gouriveau, Rafael, 2021. "Proton exchange membrane fuel cell system prognostics and decision-making: Current status and perspectives," Renewable Energy, Elsevier, vol. 179(C), pages 2277-2294.
17. Zeki Murat Çınar & Abubakar Abdussalam Nuhu & Qasim Zeeshan & Orhan Korhan & Mohammed Asmael & Babak Safaei, 2020. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0," Sustainability, MDPI, vol. 12(19), pages 1-42, October.
18. Dehghan Shoorkand, Hassan & Nourelfath, Mustapha & Hajji, Adnène, 2024. "A hybrid CNN-LSTM model for joint optimization of production and imperfect predictive maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
19. Abdullah Caliskan & Conor O’Brien & Krishna Panduru & Joseph Walsh & Daniel Riordan, 2023. "An Efficient Siamese Network and Transfer Learning-Based Predictive Maintenance System for More Sustainable Manufacturing," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
20. Mandelli, Diego & Wang, Congjian & Agarwal, Vivek & Lin, Linyu & Manjunatha, Koushik A., 2024. "Reliability modeling in a predictive maintenance context: A margin-based approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
21. Zhou, Tuqiang & Wu, Wanting & Peng, Liqun & Zhang, Mingyang & Li, Zhixiong & Xiong, Yubing & Bai, Yuelong, 2022. "Evaluation of urban bus service reliability on variable time horizons using a hybrid deep learning method," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
22. Liu, Junqiang & Pan, Chunlu & Lei, Fan & Hu, Dongbin & Zuo, Hongfu, 2021. "Fault prediction of bearings based on LSTM and statistical process analysis," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
23. Soualhi, Moncef & El Koujok, Mohamed & Nguyen, Khanh T.P. & Medjaher, Kamal & Ragab, Ahmed & Ghezzaz, Hakim & Amazouz, Mouloud & Ouali, Mohamed-Salah, 2021. "Adaptive prognostics in a controlled energy conversion process based on long- and short-term predictors," Applied Energy, Elsevier, vol. 283(C).
24. Shungang Ning & Jianzhong Sun & Cui Liu & Yang Yi, 2021. "Applications of deep learning in big data analytics for aircraft complex system anomaly detection," Journal of Risk and Reliability, , vol. 235(5), pages 923-940, October.
25. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
26. Zhuang, Liangliang & Xu, Ancha & Wang, Xiao-Lin, 2023. "A prognostic driven predictive maintenance framework based on Bayesian deep learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
27. 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).
28. Jiang, Chen & Vega, Manuel A. & Todd, Michael D. & Hu, Zhen, 2022. "Model correction and updating of a stochastic degradation model for failure prognostics of miter gates," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
29. Zhou, Kai-Li & Cheng, De-Jun & Zhang, Han-Bing & Hu, Zhong-tai & Zhang, Chun-Yan, 2023. "Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
30. Cai, Yue & Teunter, Ruud H. & de Jonge, Bram, 2023. "A data-driven approach for condition-based maintenance optimization," European Journal of Operational Research, Elsevier, vol. 311(2), pages 730-738.
31. Ying Zhang & Tao Yang & Hongkuan Zhou & Dongzhen Lyu & Wei Zheng & Xianling Li, 2023. "A Prognosis Method for Condenser Fouling Based on Differential Modeling," Energies, MDPI, vol. 16(16), pages 1-23, August.