| A review of recurrent neural networks: LSTM cells and network architectures Y Yu, X Si, C Hu, J Zhang Neural computation 31 (7), 1235-1270, 2019 | 3069 | 2019 |
| Remaining useful life estimation–a review on the statistical data driven approaches XS Si, W Wang, CH Hu, DH Zhou European journal of operational research 213 (1), 1-14, 2011 | 2311 | 2011 |
| Remaining useful life estimation based on a nonlinear diffusion degradation process XS Si, W Wang, CH Hu, DH Zhou, MG Pecht IEEE Transactions on reliability 61 (1), 50-67, 2012 | 640 | 2012 |
| Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods Z Zhang, X Si, C Hu, Y Lei European Journal of Operational Research 271 (3), 775-796, 2018 | 529 | 2018 |
| A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation XS Si, W Wang, CH Hu, MY Chen, DH Zhou Mechanical Systems and Signal Processing 35 (1-2), 219-237, 2013 | 511 | 2013 |
| A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution XS Si, W Wang, MY Chen, CH Hu, DH Zhou European Journal of Operational Research 226 (1), 53-66, 2013 | 283 | 2013 |
| Remaining useful life prediction of lithium-ion batteries based on the wiener process with measurement error S Tang, C Yu, X Wang, X Guo, X Si energies 7 (2), 520-547, 2014 | 277 | 2014 |
| An adaptive prognostic approach via nonlinear degradation modeling: Application to battery data XS Si IEEE Transactions on Industrial Electronics 62 (8), 5082-5096, 2015 | 222 | 2015 |
| Estimating remaining useful life with three-source variability in degradation modeling XS Si, W Wang, CH Hu, DH Zhou IEEE Transactions on Reliability 63 (1), 167-190, 2014 | 222 | 2014 |
| A maintenance optimization model for mission-oriented systems based on Wiener degradation C Guo, W Wang, B Guo, X Si Reliability Engineering & System Safety 111 (3), 183-194, 2013 | 154 | 2013 |
| A prognostic model based on DBN and diffusion process for degrading bearing CH Hu, H Pei, XS Si, DB Du, ZN Pang, X Wang IEEE Transactions on Industrial Electronics 67 (10), 8767-8777, 2019 | 130 | 2019 |
| A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests Q Hu, XS Si, QH Zhang, AS Qin Mechanical systems and signal processing 139, 106609, 2020 | 123 | 2020 |
| Review of machine learning based remaining useful life prediction methods for equipment 裴洪， 胡昌华， 司小胜， 张建勋， 庞哲楠， 张鹏 Journal of Mechanical Engineering 55 (8), 1-13, 2019 | 104 | 2019 |
| A State-Space-Based Prognostic Model for Hidden and Age-Dependent Nonlinear Degradation Process L Feng, H Wang, X Si, H Zou IEEE Transactions on Automation Science and Engineering, 1-15, 2013 | 96 | 2013 |
| Data-driven remaining useful life prognosis techniques XS Si, ZX Zhang, CH Hu National Defense Industry Press and Springer-Verlag GmbH, Beijing, China, 2017 | 92 | 2017 |
| A residual storage life prediction approach for systems with operation state switches XS Si, CH Hu, X Kong, DH Zhou IEEE Transactions on Industrial Electronics 61 (11), 6304-6315, 2014 | 89 | 2014 |
| A novel lifetime estimation method for two-phase degrading systems JX Zhang, CH Hu, X He, XS Si, Y Liu, DH Zhou IEEE Transactions on Reliability 68 (2), 689-709, 2018 | 87 | 2018 |
| An age-and state-dependent nonlinear prognostic model for degrading systems ZX Zhang, XS Si, CH Hu IEEE Transactions on Reliability 64 (4), 1214-1228, 2015 | 86 | 2015 |
| An additive Wiener process-based prognostic model for hybrid deteriorating systems ZQ Wang, CH Hu, W Wang, XS Si IEEE Transactions on Reliability 63 (1), 208-222, 2014 | 86 | 2014 |
| Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model N Li, N Gebraeel, Y Lei, L Bian, X Si Reliability Engineering & System Safety 186, 88-100, 2019 | 85 | 2019 |
Michael G. PechtUniversity of MarylandVerified email at umd.edu
