| Application of Bayesian networks in reliability evaluation B Cai, X Kong, Y Liu, J Lin, X Yuan, H Xu, R Ji IEEE Transactions on Industrial Informatics 15 (4), 2146-2157, 2018 | 264 | 2018 |
| A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance H Shao, J Lin, L Zhang, D Galar, U Kumar Information Fusion 74, 65-76, 2021 | 252 | 2021 |
| Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder H Zhiyi, S Haidong, J Lin, C Junsheng, Y Yu Measurement 152, 107393, 2020 | 248 | 2020 |
| A review on deep learning applications in prognostics and health management L Zhang, J Lin, B Liu, Z Zhang, X Yan, M Wei Ieee Access 7, 162415-162438, 2019 | 245 | 2019 |
| Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples Z He, H Shao, P Wang, JJ Lin, J Cheng, Y Yang Knowledge-Based Systems 191, 105313, 2020 | 223 | 2020 |
| Adaptive kernel density-based anomaly detection for nonlinear systems L Zhang, J Lin, R Karim Knowledge-Based Systems 139, 50-63, 2018 | 159 | 2018 |
| Sliding window-based fault detection from high-dimensional data streams L Zhang, J Lin, R Karim IEEE Transactions on Systems, Man, and Cybernetics: Systems 47 (2), 289-303, 2016 | 132 | 2016 |
| Restoration of smart grids: Current status, challenges, and opportunities D Fan, Y Ren, Q Feng, Y Liu, Z Wang, J Lin Renewable and Sustainable Energy Reviews 143, 110909, 2021 | 122 | 2021 |
| Deep learning for track quality evaluation of high-speed railway based on vehicle-body vibration prediction S Ma, L Gao, X Liu, J Lin IEEE Access 7, 185099-185107, 2019 | 86 | 2019 |
| Reliability analysis for preventive maintenance based on classical and Bayesian semi-parametric degradation approaches using locomotive wheel-sets as a case study J Lin, J Pulido, M Asplund Reliability Engineering & System Safety 134, 143-156, 2015 | 86 | 2015 |
| A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions L Zhang, Q Fan, J Lin, Z Zhang, X Yan, C Li Engineering applications of artificial intelligence 119, 105735, 2023 | 75 | 2023 |
| An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection L Zhang, J Lin, R Karim Reliability Engineering & System Safety 142, 482-497, 2015 | 69 | 2015 |
| Reliability analysis for degradation of locomotive wheels using parametric Bayesian approach J Lin, M Asplund, A Parida Quality and Reliability Engineering International 30 (5), 657-667, 2014 | 60 | 2014 |
| A dynamic prescriptive maintenance model considering system aging and degradation B Liu, J Lin, L Zhang, U Kumar IEEE Access 7, 94931-94943, 2019 | 51 | 2019 |
| Data-driven approach to study the polygonization of high-speed railway train wheel-sets using field data of China’s HSR train Z Chi, J Lin, R Chen, S Huang Measurement 149, 107022, 2020 | 45 | 2020 |
| Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging X Li, Y Li, K Yan, H Shao, JJ Lin Reliability Engineering & System Safety 230, 108921, 2023 | 42 | 2023 |
| Evaluating the measurement capability of a wheel profile measurement system by using GR&R M Asplund, J Lin Measurement 92, 19-27, 2016 | 37 | 2016 |
| Multiscale dilated convolutional subdomain adaptation network with attention for unsupervised fault diagnosis of rotating machinery cross operating conditions Y Xiao, H Shao, Z Min, H Cao, X Chen, JJ Lin Measurement 204, 112146, 2022 | 36 | 2022 |
| Reliability of nonrepairable phased-mission systems with common bus performance sharing H Yu, J Yang, J Lin, Y Zhao Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2018 | 36 | 2018 |
| End-to-end unsupervised fault detection using a flow-based model L Zhang, J Lin, H Shao, Z Zhang, X Yan, J Long Reliability Engineering & System Safety 215, 107805, 2021 | 31 | 2021 |
