| Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data WJ Lee, H Wu, H Yun, H Kim, MBG Jun, JW Sutherland Procedia Cirp 80, 506-511, 2019 | 182 | 2019 |
| Avoiding environmental consequences of equipment failure via an LSTM-based model for predictive maintenance H Wu, A Huang, JW Sutherland Procedia Manufacturing 43, 666-673, 2020 | 33 | 2020 |
| Learning via acceleration spectrograms of a DC motor system with application to condition monitoring WJ Lee, H Wu, A Huang, JW Sutherland The International Journal of Advanced Manufacturing Technology 106, 803-816, 2020 | 24 | 2020 |
| A transformer-based approach for novel fault detection and fault classification/diagnosis in manufacturing: A rotary system application H Wu, MJ Triebe, JW Sutherland Journal of Manufacturing Systems 67, 439-452, 2023 | 22 | 2023 |
| Layer-wise relevance propagation for interpreting LSTM-RNN decisions in predictive maintenance H Wu, A Huang, JW Sutherland The International Journal of Advanced Manufacturing Technology, 2021 | 20 | 2021 |
| Perspectives on future research directions in green manufacturing for discrete products MJ Triebe, S Deng, JR Pérez-Cardona, BG Joung, H Wu, N Shakelly, ... Green Manufacturing Open 1 (IS-J 11,041), 2023 | 5 | 2023 |
| Condition-Based Monitoring and Novel Fault Detection Based on Incremental Learning Applied to Rotary Systems H Wu, A Huang, JW Sutherland Procedia CIRP 105, 788-793, 2022 | 5 | 2022 |
| Enhancing Interpretability and Adaptability of Manufacturing Equipment Health Models and Establishment of Cost Models for Maintenance Decisions H Wu Purdue University, 2023 | | 2023 |
| A review of research on smart manufacturing in support of environmental sustainability A Huang, M Triebe, Z Li, H Wu, BG Joung, JW Sutherland International Journal of Sustainable Manufacturing 5 (2-4), 132-163, 2022 | | 2022 |
John W. SutherlandEnvironmental and Ecological Engineering, Purdue UniversityE-mail confirmado em purdue.edu
