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Remaining useful life estimation in prognostics using deep convolution neural networks

Citations

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Cited by:

  1. 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).
  2. Miao, Mengqi & Yu, Jianbo & Zhao, Zhihong, 2022. "A sparse domain adaption network for remaining useful life prediction of rolling bearings under different working conditions," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
  3. Xiang, Sheng & Li, Penghua & Huang, Yi & Luo, Jun & Qin, Yi, 2024. "Single gated RNN with differential weighted information storage mechanism and its application to machine RUL prediction," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
  4. Dai, Le & Guo, Junyu & Wan, Jia-Lun & Wang, Jiang & Zan, Xueping, 2022. "A reliability evaluation model of rolling bearings based on WKN-BiGRU and Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
  5. Hervé de Beaulieu, Martin & Jha, Mayank Shekhar & Garnier, Hugues & Cerbah, Farid, 2024. "Remaining Useful Life prediction based on physics-informed data augmentation," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
  6. Listou Ellefsen, André & Bjørlykhaug, Emil & Æsøy, Vilmar & Ushakov, Sergey & Zhang, Houxiang, 2019. "Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 240-251.
  7. Xu, Danyang & Qiu, Haobo & Gao, Liang & Yang, Zan & Wang, Dapeng, 2022. "A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
  8. Basora, Luis & Viens, Arthur & Chao, Manuel Arias & Olive, Xavier, 2025. "A benchmark on uncertainty quantification for deep learning prognostics," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
  9. 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).
  10. Zhu, Yongmeng & Wu, Jiechang & Wu, Jun & Liu, Shuyong, 2022. "Dimensionality reduce-based for remaining useful life prediction of machining tools with multisensor fusion," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
  11. Li, Xinliang & Zhang, Wan & Ding, Yu & Cai, Jun & Yan, Xiaoan, 2025. "Rolling bearing degradation stage division and RUL prediction based on recursive exponential slow feature analysis and Bi-LSTM model," Reliability Engineering and System Safety, Elsevier, vol. 259(C).
  12. 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).
  13. Liang, Tao & Wang, Fuli & Wang, Shu & Li, Kang & Mo, Xuelei & Lu, Di, 2024. "Machinery health prognostic with uncertainty for mineral processing using TSC-TimeGAN," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
  14. Asgari, Ali & Si, Wujun & Yuan, Liang & Krishnan, Krishna & Wei, Wei, 2024. "Multivariable degradation modeling and life prediction using multivariate fractional Brownian motion," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
  15. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
  16. Yi, Tongqiang & Guo, Jiang & Meng, Yang & Ling, Yuewei & Ke, Yiming & Guo, Zhilong, 2026. "MSTI-GNN: A multi-scale spatiotemporal interactive graph neural network method for precise hydropower unit status prediction," Renewable Energy, Elsevier, vol. 256(PB).
  17. Xu, Zhiqiang & Zhang, Yujie & Miao, Qiang, 2024. "An attention-based multi-scale temporal convolutional network for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
  18. Bai, Guo-Peng & Er, Guo-Kang & Iu, Vai Pan, 2024. "A novel stochastic approach to investigate the probabilistic characteristics of the ship roll system with sinusoidal restoring force," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
  19. Yan, Shen & Shao, Haidong & Min, Zhishan & Peng, Jiangji & Cai, Baoping & Liu, Bin, 2023. "FGDAE: A new machinery anomaly detection method towards complex operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
  20. Ye, Lin & Wang, Chengyou & Zhou, Xiao & Jiang, Baocheng & Yu, Changsong & Qin, Zhiliang, 2025. "Natural gas pipeline weak leakage detection based on negative pressure wave decomposition and feature enhancement," Reliability Engineering and System Safety, Elsevier, vol. 257(PB).
  21. Lin, Yan-Hui & Chang, Liang & Guan, Lu-Xin, 2024. "Enhanced stochastic recurrent hybrid model for RUL Predictions via Semi-supervised learning," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
  22. Shahil Kumar & Krish Kumar Raj & Maurizio Cirrincione & Giansalvo Cirrincione & Vincenzo Franzitta & Rahul Ranjeev Kumar, 2024. "A Comprehensive Review of Remaining Useful Life Estimation Approaches for Rotating Machinery," Energies, MDPI, vol. 17(22), pages 1-46, November.
  23. 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).
  24. Chang, Yang & Fang, Huajing, 2019. "A hybrid prognostic method for system degradation based on particle filter and relevance vector machine," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 51-63.
  25. Zhang, Chao & Gong, Daqing & Xue, Gang, 2025. "An uncertainty-incorporated active data diffusion learning framework for few-shot equipment RUL prediction," Reliability Engineering and System Safety, Elsevier, vol. 254(PB).
  26. He, Rui & Tian, Zhigang & Wang, Yifei & Zuo, Mingjian & Guo, Ziwei, 2023. "Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
  27. John Taco & Pradeep Kundu & Jay Lee, 2024. "A novel technique for multiple failure modes classification based on deep forest algorithm," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3115-3129, October.
  28. Zheng, Minglei & Man, Junfeng & Wang, Dian & Chen, Yanan & Li, Qianqian & Liu, Yong, 2023. "Semi-supervised multivariate time series anomaly detection for wind turbines using generator SCADA data," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
  29. Zhang, Caiping & Wang, Yubin & Gao, Yang & Wang, Fang & Mu, Biqiang & Zhang, Weige, 2019. "Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method," Applied Energy, Elsevier, vol. 256(C).
  30. Li, Xuanlin & Hu, Yawei & Wang, Hang & Liu, Yongbin & Liu, Xianzeng & Lu, Huitian, 2025. "A closed-form continuous-depth neural-based hybrid difference features re-representation network for RUL prediction," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
  31. Yu Mo & Liang Li & Biqing Huang & Xiu Li, 2023. "Few-shot RUL estimation based on model-agnostic meta-learning," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2359-2372, June.
  32. Apostolos Giannoulidis & Anastasios Gounaris & Athanasios Naskos & Nikodimos Nikolaidis & Daniel Caljouw, 2025. "Engineering and evaluating an unsupervised predictive maintenance solution: a cold-forming press case-study," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 2121-2139, March.
  33. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
  34. Lee, Juseong & Mitici, Mihaela, 2022. "Multi-objective design of aircraft maintenance using Gaussian process learning and adaptive sampling," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
  35. Li, Yuanfu & Chen, Yifan & Shao, Haonan & Zhang, Huisheng, 2023. "A novel dual attention mechanism combined with knowledge for remaining useful life prediction based on gated recurrent units," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
  36. Ma’d El-Dalahmeh & Maher Al-Greer & Mo’ath El-Dalahmeh & Michael Short, 2020. "Time-Frequency Image Analysis and Transfer Learning for Capacity Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 13(20), pages 1-19, October.
  37. Vrignat, Pascal & Kratz, Frédéric & Avila, Manuel, 2022. "Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
  38. 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).
  39. 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).
  40. Yuyan Yin & Jie Tian & Xinfeng Liu, 2025. "Remaining useful life prediction based on parallel multi-scale feature fusion network," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3111-3127, June.
  41. Chen, Zhixiang, 2025. "Machine remaining useful life prediction method based on global-local attention compensation network," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
  42. Feiyue Deng & Yan Bi & Yongqiang Liu & Shaopu Yang, 2021. "Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network," Mathematics, MDPI, vol. 9(23), pages 1-17, November.
  43. 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).
  44. Guishuang Tian & Shaoping Wang & Jian Shi & Yajing Qiao, 2022. "State Estimation and Remaining Useful Life Prediction of PMSTM Based on a Combination of SIR and HSMM," Sustainability, MDPI, vol. 14(24), pages 1-21, December.
  45. 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).
  46. Zhou, Jie & Lin, Haifei & Li, Shugang & Jin, Hongwei & Zhao, Bo & Liu, Shihao, 2023. "Leakage diagnosis and localization of the gas extraction pipeline based on SA-PSO BP neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
  47. 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).
  48. Manuel Arias Chao & Chetan Kulkarni & Kai Goebel & Olga Fink, 2021. "Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics," Data, MDPI, vol. 6(1), pages 1-14, January.
  49. Zhu, Qixiang & Zhou, Zheng & Li, Yasong & Yan, Ruqiang, 2024. "Contrastive BiLSTM-enabled Health Representation Learning for Remaining Useful Life Prediction," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
  50. Hu, Tao & Guo, Yiming & Gu, Liudong & Zhou, Yifan & Zhang, Zhisheng & Zhou, Zhiting, 2022. "Remaining useful life prediction of bearings under different working conditions using a deep feature disentanglement based transfer learning method," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
  51. 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).
  52. Chen, Chong & Liu, Ying & Sun, Xianfang & Cairano-Gilfedder, Carla Di & Titmus, Scott, 2021. "An integrated deep learning-based approach for automobile maintenance prediction with GIS data," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
  53. 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).
  54. Marcin Witczak & Marcin Mrugalski & Bogdan Lipiec, 2021. "Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework," Energies, MDPI, vol. 14(8), pages 1-23, April.
  55. 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).
  56. 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).
  57. Anlan Ding & Baoliang Peng & Ke Yang & Yanhua Zhang & Xiaoxuan Yang & Xiuguo Zou & Zhangqing Zhu, 2022. "Design of a Machine Vision-Based Automatic Digging Depth Control System for Garlic Combine Harvester," Agriculture, MDPI, vol. 12(12), pages 1-19, December.
  58. Berndt Jesenko & Christian Schlögl, 2021. "The effect of web of science subject categories on clustering: the case of data-driven methods in business and economic sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6785-6801, August.
  59. Wang, Wei & Song, Honghao & Si, Shubin & Lu, Wenhao & Cai, Zhiqiang, 2024. "Data augmentation based on diffusion probabilistic model for remaining useful life estimation of aero-engines," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
  60. 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).
  61. Ding, Shousheng & Meng, Lei & Shang, Jie & Jiang, Chen & Qiu, Haobo & Gao, Liang, 2025. "A soft actor-critic reinforcement learning-based method for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
  62. Andriotis, C.P. & Papakonstantinou, K.G., 2019. "Managing engineering systems with large state and action spaces through deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
  63. Chen, Xi & Wang, Hui & Lu, Siliang & Xu, Jiawen & Yan, Ruqiang, 2023. "Remaining useful life prediction of turbofan engine using global health degradation representation in federated learning," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
  64. Tengda Huang & Sheng Fu & Haonan Feng & Jiafeng Kuang, 2019. "Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention," Energies, MDPI, vol. 12(20), pages 1-19, October.
  65. Youdao Wang & Yifan Zhao, 2022. "Multi-Scale Remaining Useful Life Prediction Using Long Short-Term Memory," Sustainability, MDPI, vol. 14(23), pages 1-19, November.
  66. 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).
  67. Zhang, Jiusi & Jiang, Yuchen & Wu, Shimeng & Li, Xiang & Luo, Hao & Yin, Shen, 2022. "Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
  68. Lin, Chaojing & Chen, Yunxiao & Bai, Mingliang & Long, Zhenhua & Yao, Peng & Liu, Jinfu & Yu, Daren, 2025. "Improved multiple penalty mechanism based loss function for more realistic aeroengine RUL advanced prediction," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
  69. Liu, Ze-Zhou & Sun, Tao & Sun, Xi-Ming, 2025. "A spatial–temporal graph structure automatic feedback learning system with tensor fusion and its application on engine RUL prediction," Energy, Elsevier, vol. 334(C).
  70. Dourado, Arinan & Viana, Felipe A.C., 2021. "Early life failures and services of industrial asset fleets," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
  71. Fan, Zihan & Chen, Yongyi & Liu, Xuezhen & Zhang, Dan, 2025. "A two-stage subdomain adaptation network for machinery remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).
  72. Chengcheng Fu & Cheng Gao & Weifang Zhang, 2024. "RUL Prediction for Piezoelectric Vibration Sensors Based on Digital-Twin and LSTM Network," Mathematics, MDPI, vol. 12(8), pages 1-27, April.
  73. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Luo, Hao & Yin, Shen, 2023. "An integrated multi-head dual sparse self-attention network for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
  74. Li, Xiang & Zhang, Wei & Ding, Qian, 2019. "Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 208-218.
  75. 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).
  76. 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).
  77. 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).
  78. Pengcheng Xia & Yixiang Huang & Chengjin Qin & Chengliang Liu, 2024. "Towards prognostic generalization: a domain conditional invariance and specificity disentanglement network for remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3459-3477, October.
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  81. Andrés Ruiz-Tagle Palazuelos & Enrique López Droguett & Rodrigo Pascual, 2020. "A novel deep capsule neural network for remaining useful life estimation," Journal of Risk and Reliability, , vol. 234(1), pages 151-167, February.
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  83. Zhengyang Fan & Wanru Li & Kuo-Chu Chang, 2023. "A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation," Mathematics, MDPI, vol. 11(24), pages 1-17, December.
  84. Huang, Zhifu & Yang, Yang & Hu, Yawei & Ding, Xiang & Li, Xuanlin & Liu, Yongbin, 2023. "Attention-augmented recalibrated and compensatory network for machine remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
  85. 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).
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  87. Zhou, Maohui & Li, Yanjun & Cao, Yuyuan & Ma, Xinyu & Xu, Zhenteng, 2025. "Physics-informed spatio-temporal hybrid neural networks for predicting remaining useful life in aircraft engine," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
  88. Chen, Jiaxian & Li, Dongpeng & Huang, Ruyi & Chen, Zhuyun & Li, Weihua, 2023. "Aero-engine remaining useful life prediction method with self-adaptive multimodal data fusion and cluster-ensemble transfer regression," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
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  91. Zhao, Hongqian & Chen, Zheng & Shu, Xing & Xiao, Renxin & Shen, Jiangwei & Liu, Yu & Liu, Yonggang, 2024. "Online surface temperature prediction and abnormal diagnosis of lithium-ion batteries based on hybrid neural network and fault threshold optimization," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
  92. Zhang, Mingyuan & He, Chen & Huang, Chengxuan & Yang, Jianhong, 2024. "A weighted time embedding transformer network for remaining useful life prediction of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
  93. Wu, Jinxin & He, Deqiang & Li, Jiayi & Miao, Jian & Li, Xianwang & Li, Hongwei & Shan, Sheng, 2024. "Temporal multi-resolution hypergraph attention network for remaining useful life prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
  94. Nejjar, Ismail & Geissmann, Fabian & Zhao, Mengjie & Taal, Cees & Fink, Olga, 2024. "Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
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  101. Wang, Enxiu & Lei, Zihao & Wen, Guangrui & Liu, Zimin & Su, Yu & Zhang, Zhifen & Chen, Xuefeng, 2026. "A physics-constrained Bayesian neural network for machinery remaining useful life prediction and uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 266(PB).
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  107. Wang, Bingsen & Baraldi, Piero & Shokry, Ahmed & Zio, Enrico, 2026. "A novel two-stage heterogeneous transfer learning framework for the estimation of the remaining useful life of industrial components," Reliability Engineering and System Safety, Elsevier, vol. 267(PB).
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  109. Fan, Linchuan & Chai, Yi & Chen, Xiaolong, 2022. "Trend attention fully convolutional network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
  110. Nadire Cavus & Yakubu Bala Mohammed & Mohammed Nasiru Yakubu, 2021. "An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps," Sustainability, MDPI, vol. 13(11), pages 1-21, May.
  111. 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).
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