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Li, Zhe
Li, Zhe
Shanghai Electric Group
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Cited by
Year
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
3452017
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
1742019
Industry 4.0-potentials for predictive maintenance
Z Li, K Wang, Y He
6th international workshop of advanced manufacturing and automation, 42-46, 2016
932016
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
812019
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
672020
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
642015
Deep transfer learning for failure prediction across failure types
Z Li, E Kristoffersen, J Li
Computers & Industrial Engineering 172, 108521, 2022
172022
A survey of deep learning-driven architecture for predictive maintenance
Z Li, Q He, J Li
Engineering Applications of Artificial Intelligence 133, 108285, 2024
112024
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
112020
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
62018
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
52016
Knowledge discovery and anomaly identification for low correlation industry data
Z Li, J Li
Advanced Manufacturing and Automation IX 9th, 195-202, 2020
22020
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
22019
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
12017
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
12016
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
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