| A deep feature learning method for remaining useful life prediction of drilling pumps J Guo, JL Wan, Y Yang, L Dai, A Tang, B Huang, F Zhang, H Li Energy 282, 128442, 2023 | 57 | 2023 |
| Supportive emergency decision-making model towards sustainable development with fuzzy expert system H Li, JY Guo, M Yazdi, A Nedjati, KA Adesina Neural Computing and Applications 33 (22), 15619-15637, 2021 | 55 | 2021 |
| A reliability evaluation model of rolling bearings based on WKN-BiGRU and Wiener process L Dai, J Guo, JL Wan, J Wang, X Zan Reliability Engineering & System Safety 225, 108646, 2022 | 54 | 2022 |
| A hybrid prognosis scheme for rolling bearings based on a novel health indicator and nonlinear Wiener process J Guo, Z Wang, H Li, Y Yang, CG Huang, M Yazdi, HS Kang Reliability Engineering & System Safety 245, 110014, 2024 | 49 | 2024 |
| A parallel deep neural network for intelligent fault diagnosis of drilling pumps J Guo, Y Yang, H Li, L Dai, B Huang Engineering Applications of Artificial Intelligence 133, 108071, 2024 | 44 | 2024 |
| A CNN‐BiLSTM‐Bootstrap integrated method for remaining useful life prediction of rolling bearings J Guo, J Wang, Z Wang, Y Gong, J Qi, G Wang, C Tang Quality and Reliability Engineering International 39 (5), 1796-1813, 2023 | 34 | 2023 |
| A hybrid intelligent rolling bearing fault diagnosis method combining WKN-BiLSTM and attention mechanism J Wang, J Guo, L Wang, Y Yang, Z Wang, R Wang Measurement Science and Technology 34 (8), 085106, 2023 | 31 | 2023 |
| Bayesian degradation assessment of CNC machine tools considering unit non-homogeneity J Guo, YF Li, B Zheng, HZ Huang Journal of Mechanical Science and Technology 32, 2479-2485, 2018 | 30 | 2018 |
| Reliability assessment of non-repairable multi-state phased mission systems with backup missions XY Li, X Xiong, J Guo, HZ Huang, X Li Reliability Engineering & System Safety 223, 108462, 2022 | 28 | 2022 |
| A deep learning based health indicator construction and fault prognosis with uncertainty quantification for rolling bearings Z Wang, J Guo, J Wang, Y Yang, L Dai, CG Huang, JL Wan Measurement Science and Technology 34 (10), 105105, 2023 | 24 | 2023 |
| A random forest regression with Bayesian optimization-based method for fatigue strength prediction of ferrous alloys J Guo, X Zan, L Wang, L Lei, C Ou, S Bai Engineering Fracture Mechanics 293, 109714, 2023 | 22 | 2023 |
| Bayesian information fusion method for reliability analysis with failure‐time data and degradation data J Guo, YF Li, W Peng, HZ Huang Quality and Reliability Engineering International 38 (4), 1944-1956, 2022 | 20 | 2022 |
| Bayesian information fusion for degradation analysis of deteriorating products with individual heterogeneity J Guo, HZ Huang, W Peng, J Zhou Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2019 | 17 | 2019 |
| A hybrid deep learning model towards fault diagnosis of drilling pump J Guo, Y Yang, H Li, J Wang, A Tang, D Shan, B Huang Applied Energy 372, 123773, 2024 | 14 | 2024 |
| Current signal characteristics analysis of transmission system in high-speed train under abnormal vibration conditions H Wu, P Wu, J Guo, S Zhou, J Wei Vehicle System Dynamics 61 (4), 1151-1167, 2023 | 13 | 2023 |
| Influence of DC-link voltage pulsation of transmission systems on mechanical structure vibration and fatigue in high-speed trains P Wu, J Guo, H Wu, J Wei Engineering Failure Analysis 130, 105772, 2021 | 13 | 2021 |
| Experimental study of methane hydrate formation and agglomeration in waxy oil-in-water emulsions L Wang, J Chen, T Ma, J Jing, L Lei, J Guo Energy 288, 129945, 2024 | 6 | 2024 |
| A framework for fatigue reliability analysis of high-pressure turbine blades J Zhou, HZ Huang, YF Li, J Guo Annals of Operations Research, 1-17, 2022 | 6 | 2022 |
| An integrated deep learning model for intelligent recognition of long-distance natural gas pipeline features L Wang, W Guo, J Guo, S Zheng, Z Wang, HS Kang, H Li Reliability Engineering & System Safety 255, 110664, 2025 | 5 | 2025 |
| GTFE-Net-BiLSTM-AM: An intelligent feature recognition method for natural gas pipelines L Wang, C Hu, T Ma, Z Yang, W Guo, Z Mao, J Guo, H Li Gas Science and Engineering 125, 205311, 2024 | 5 | 2024 |