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Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture

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  1. Yan, Jianhai & He, Zhen & He, Shuguang, 2023. "Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
  2. Li, Tianmei & Pei, Hong & Si, Xiaosheng & Lei, Yaguo, 2023. "Prognosis for stochastic degrading systems with massive data: A data-model interactive perspective," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
  3. Kamei, Sayaka & Taghipour, Sharareh, 2023. "A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
  4. Xu, Huyang & Fard, Nasser & Fang, Yuanchen, 2020. "Time series chain graph for modeling reliability covariates in degradation process," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
  5. Fan, Yuantao & Nowaczyk, Sławomir & Rögnvaldsson, Thorsteinn, 2020. "Transfer learning for remaining useful life prediction based on consensus self-organizing models," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
  6. 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).
  7. Bae, Jinwoo & Xi, Zhimin, 2022. "Learning of physical health timestep using the LSTM network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
  8. Li, Xingyu & Krivtsov, Vasiliy & Arora, Karunesh, 2022. "Attention-based deep survival model for time series data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
  9. Arias Chao, Manuel & Kulkarni, Chetan & Goebel, Kai & Fink, Olga, 2022. "Fusing physics-based and deep learning models for prognostics," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
  10. He, Yuxuan & Su, Huai & Zio, Enrico & Peng, Shiliang & Fan, Lin & Yang, Zhaoming & Yang, Zhe & Zhang, Jinjun, 2023. "A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
  11. Wei, Yupeng & Wu, Dazhong & Terpenny, Janis, 2021. "Learning the health index of complex systems using dynamic conditional variational autoencoders," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
  12. Yu Mo & Qianhui Wu & Xiu Li & Biqing Huang, 2021. "Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1997-2006, October.
  13. Xiao, Lei & Tang, Junxuan & Zhang, Xinghui & Bechhoefer, Eric & Ding, Siyi, 2021. "Remaining useful life prediction based on intentional noise injection and feature reconstruction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
  14. Jiao, Ruihua & Peng, Kaixiang & Dong, Jie & Zhang, Chuanfang, 2020. "Fault monitoring and remaining useful life prediction framework for multiple fault modes in prognostics," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
  15. Zhang, Wei & Li, Xiang & Ma, Hui & Luo, Zhong & Li, Xu, 2021. "Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
  16. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
  17. Xu, Dan & Xiao, Xiaoqi & Liu, Jie & Sui, Shaobo, 2023. "Spatio-temporal degradation modeling and remaining useful life prediction under multiple operating conditions based on attention mechanism and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
  18. 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).
  19. Wang, Yuan & Lei, Yaguo & Li, Naipeng & Yan, Tao & Si, Xiaosheng, 2023. "Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
  20. 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).
  21. Li, Yuanfu & Chen, Yao & Hu, Zhenchao & Zhang, Huisheng, 2023. "Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
  22. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Fang, Xiaolei & Cai, Xiao & Yan, Tao, 2021. "Remaining useful life prediction based on a multi-sensor data fusion model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
  23. Zhang, Yadong & Zhang, Chao & Wang, Shaoping & Dui, Hongyan & Chen, Rentong, 2024. "Health indicators for remaining useful life prediction of complex systems based on long short-term memory network and improved particle filter," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
  24. Kong, Ziqian & Jin, Xiaohang & Xu, Zhengguo & Chen, Zian, 2023. "A contrastive learning framework enhanced by unlabeled samples for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
  25. 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).
  26. Li, Xin & Li, Yong & Yan, Ke & Shao, Haidong & (Jing) Lin, Janet, 2023. "Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  27. Azar, Kamyar & Hajiakhondi-Meybodi, Zohreh & Naderkhani, Farnoosh, 2022. "Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
  28. Liu, Lu & Song, Xiao & Zhou, Zhetao, 2022. "Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
  29. 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.
  30. Aremu, Oluseun Omotola & Hyland-Wood, David & McAree, Peter Ross, 2020. "A machine learning approach to circumventing the curse of dimensionality in discontinuous time series machine data," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
  31. de Pater, Ingeborg & Reijns, Arthur & Mitici, Mihaela, 2022. "Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
  32. Zhang, Yong & Xin, Yuqi & Liu, Zhi-wei & Chi, Ming & Ma, Guijun, 2022. "Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
  33. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
  34. Prakash, Om & Samantaray, Arun Kumar, 2021. "Prognosis of Dynamical System Components with Varying Degradation Patterns using model–data–fusion," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
  35. Yu, Wennian & Kim, II Yong & Mechefske, Chris, 2020. "An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
  36. Sánchez, Luciano & Costa, Nahuel & Couso, Inés, 2023. "Simplified models of remaining useful life based on stochastic orderings," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
  37. da Costa, Paulo Roberto de Oliveira & Akçay, Alp & Zhang, Yingqian & Kaymak, Uzay, 2020. "Remaining useful lifetime prediction via deep domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
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