| Prognostics and health management: A review from the perspectives of design, development and decision Y Hu, X Miao, Y Si, E Pan, E Zio Reliability Engineering & System Safety 217, 108063, 2022 | 135 | 2022 |
| A particle filtering and kernel smoothing-based approach for new design component prognostics Y Hu, P Baraldi, F Di Maio, E Zio Reliability Engineering & System Safety 134, 19-31, 2015 | 131 | 2015 |
| Fault diagnostics between different type of components: A transfer learning approach X Li, Y Hu, M Li, J Zheng Applied Soft Computing 86, 105950, 2020 | 96 | 2020 |
| Online Performance Assessment Method for a Model-Based Prognostic Approach Y Hu, P Baraldi, F Di Maio, E Zio IEEE Transactions on Reliability 65 (2), 718-735, 2016 | 73 | 2016 |
| Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications Y Lin, X Li, Y Hu Applied Soft Computing 72, 555-564, 2018 | 63 | 2018 |
| Feature learning for fault detection in high-dimensional condition monitoring signals G Michau, Y Hu, T Palmé, O Fink Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2020 | 62 | 2020 |
| Central moment discrepancy based domain adaptation for intelligent bearing fault diagnosis X Li, Y Hu, J Zheng, M Li, W Ma Neurocomputing 429, 12-24, 2021 | 60 | 2021 |
| Reinforcement learning-driven maintenance strategy: A novel solution for long-term aircraft maintenance decision optimization Y Hu, X Miao, J Zhang, J Liu, E Pan Computers & industrial engineering 153, 107056, 2021 | 55 | 2021 |
| A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment Y Hu, P Baraldi, F Di Maio, E Zio Mechanical Systems and Signal Processing 88, 413-427, 2017 | 44 | 2017 |
| Fault detection based on signal reconstruction with auto-associative extreme learning machines Y Hu, T Palmé, O Fink Engineering applications of artificial intelligence 57, 105-117, 2017 | 43 | 2017 |
| Predicting railway wheel wear under uncertainty of wear coefficient, using universal kriging MA Cremona, B Liu, Y Hu, S Bruni, R Lewis Reliability Engineering & System Safety 154, 49-59, 2016 | 41 | 2016 |
| A SVM-based framework for fault detection in high-speed trains J Liu, Y Hu, S Yang Measurement 172, 108779, 2021 | 33 | 2021 |
| One-shot neural architecture search for fault diagnosis using vibration signals X Li, J Zheng, M Li, W Ma, Y Hu Expert Systems with Applications 190, 116027, 2022 | 27 | 2022 |
| Deep health indicator extraction: A method based on auto-encoders and extreme learning machines Y Hu, T Palmé, O Fink Annual Conference of the PHM Society 8 (1), 2016 | 24 | 2016 |
| Frequency-domain fusing convolutional neural network: A unified architecture improving effect of domain adaptation for fault diagnosis X Li, J Zheng, M Li, W Ma, Y Hu Sensors 21 (2), 450, 2021 | 17 | 2021 |
| System risk evolution analysis and risk critical event identification based on event sequence diagram P Luo, Y Hu Reliability Engineering & System Safety 114, 36-44, 2013 | 16 | 2013 |
| Neural architecture search for fault diagnosis X Li, Y Hu, J Zheng, M Li arXiv preprint arXiv:2002.07997, 2020 | 10 | 2020 |
| Particle filtering for prognostics of a newly designed product with a new parameters initialization strategy based on reliability test data J Liu, E Zio, Y Hu IEEE Access 6, 62564-62573, 2018 | 8 | 2018 |
| A Compacted Object Sample Extraction (COMPOSE)-based method for fault diagnostics in evolving environment Y Hu, P Baraldi, F Di Maio, E Zio 2015 Prognostics and System Health Management Conference (PHM), 1-5, 2015 | 8 | 2015 |
| Performance data prognostics based on relevance vector machine and particle filter Y Hu, P Luob Chemical Engineering 33, 349-354, 2013 | 7 | 2013 |
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