| Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario Z Li, Y Wang, KS Wang Advances in Manufacturing 5, 377-387, 2017 | 342 | 2017 |
| A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment Z Li, J Li, Y Wang, K Wang The International Journal of Advanced Manufacturing Technology 103, 499-510, 2019 | 174 | 2019 |
| Industry 4.0-potentials for predictive maintenance Z Li, K Wang, Y He 6th international workshop of advanced manufacturing and automation, 42-46, 2016 | 93 | 2016 |
| A deep learning driven method for fault classification and degradation assessment in mechanical equipment Z Li, Y Wang, K Wang Computers in industry 104, 1-10, 2019 | 81 | 2019 |
| A data-driven method based on deep belief networks for backlash error prediction in machining centers Z Li, Y Wang, K Wang Journal of Intelligent Manufacturing 31, 1693-1705, 2020 | 67 | 2020 |
| Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs KS Wang, Z Li, J Braaten, Q Yu Advances in Manufacturing 3, 97-104, 2015 | 64 | 2015 |
| Deep transfer learning for failure prediction across failure types Z Li, E Kristoffersen, J Li Computers & Industrial Engineering 172, 108521, 2022 | 17 | 2022 |
| Smart maintenance in asset management–application with deep learning H Rødseth, RJ Eleftheriadis, Z Li, J Li Advanced Manufacturing and Automation IX 9th, 608-615, 2020 | 11 | 2020 |
| A survey of deep learning-driven architecture for predictive maintenance Z Li, Q He, J Li Engineering Applications of Artificial Intelligence 133, 108285, 2024 | 10 | 2024 |
| Deep learning driven approaches for predictive maintenance: A framework of intelligent fault diagnosis and prognosis in the industry 4.0 era Z Li NTNU, 2018 | 6 | 2018 |
| A review on data-driven predictive maintenance approach for hydro turbines/generators S Wang, K Wang, Z Li 6th International Workshop of Advanced Manufacturing and Automation, 30-35, 2016 | 5 | 2016 |
| Knowledge discovery and anomaly identification for low correlation industry data Z Li, J Li Advanced Manufacturing and Automation IX 9th, 195-202, 2020 | 2 | 2020 |
| HDPS-BPSO based predictive maintenance scheduling for backlash error compensation in a machining center Z Li, Y Wang, K Wang, J Li Advanced Manufacturing and Automation VIII 8, 71-77, 2019 | 2 | 2019 |
| Fault classification and degradation assessment based on wavelet packet decomposition for rotary machinery Z Li, VGB Pedersen, K Wang, Y He International Workshop of Advanced Manufacturing and Automation, 509-516, 2017 | 1 | 2017 |
| Applying Radial Basis Function Networks to Fault Diagnosis of Motorized Spindle Z Li, K Wang, J Yang, Y Stefanov 6th International Workshop of Advanced Manufacturing and Automation, 237-240, 2016 | 1 | 2016 |
| A Hybrid Data-driven Method to Identify Abnormal Energy Consumption during Production Z Li, S Gu, L Huang 2024 IEEE 9th International Conference on Computational Intelligence and …, 2024 | | 2024 |
| Design of Graphical Configuration Modeling System Based on Fault Diagnosis Class Y Xu, Z Li International Workshop of Advanced Manufacturing and Automation, 8-15, 2023 | | 2023 |
| Sensor Failure Identification in Industrial Big Data Z Li, CG Dahling, J Li, W Xu 2021 7th International Conference on Condition Monitoring of Machinery in …, 2021 | | 2021 |
| Comparing different methods of fault classification in centrifugal pumps Z Li, K Wang WIT Transactions on Engineering Sciences 113, 217-224, 2016 | | 2016 |
| Research on fault diagnosis and prognosis for machine centers Z Li, K Wang WIT Transactions on Engineering Sciences 113, 225-232, 2016 | | 2016 |
