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 | 282 | 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 | 139 | 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 | 85 | 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 | 73 | 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 | 62 | 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 | 58 | 2015 |
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 | 8 | 2020 |
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 |
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 | 2 | 2018 |
Fault classification and degradation assessment based on wavelet packet decomposition for rotary machinery Z Li, VGB Pedersen, K Wang, Y He Advanced Manufacturing and Automation VII 7, 509-516, 2018 | 1 | 2018 |
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 | | 2016 |
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 |