| Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump M Zhang, Z Jiang, K Feng Mechanical Systems and Signal Processing 93, 460-493, 2017 | 394 | 2017 |
| Metric-based meta-learning model for few-shot fault diagnosis under multiple limited data conditions D Wang, M Zhang, Y Xu, W Lu, J Yang, T Zhang Mechanical Systems and Signal Processing 155, 107510, 2021 | 176 | 2021 |
| A deep transfer model with wasserstein distance guided multi-adversarial networks for bearing fault diagnosis under different working conditions M Zhang, D Wang, W Lu, J Yang, Z Li, B Liang IEEE access 7, 65303-65318, 2019 | 128 | 2019 |
| Dynamic scheduling method for job-shop manufacturing systems by deep reinforcement learning with proximal policy optimization M Zhang, Y Lu, Y Hu, N Amaitik, Y Xu sustainability 14 (9), 5177, 2022 | 47 | 2022 |
| Predictive maintenance for remanufacturing based on hybrid-driven remaining useful life prediction M Zhang, N Amaitik, Z Wang, Y Xu, A Maisuradze, M Peschl, D Tzovaras Applied Sciences 12 (7), 3218, 2022 | 31 | 2022 |
| Remaining useful life estimation for turbofan engine with transformer-based deep architecture Q Ma, M Zhang, Y Xu, J Song, T Zhang 2021 26th international conference on automation and computing (ICAC), 1-6, 2021 | 29 | 2021 |
| Wasserstein distance guided adversarial imitation learning with reward shape exploration M Zhang, Y Wang, X Ma, L Xia, J Yang, Z Li, X Li 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS …, 2020 | 20 | 2020 |
| A novel deep model with meta-learning for rolling bearing few-shot fault diagnosis X Liang, M Zhang, G Feng, Y Xu, D Zhen, F Gu Journal of Dynamics, Monitoring and Diagnostics 2 (2), 102-114, 2023 | 19 | 2023 |
| Reliability enhancement of state of health assessment model of lithium-ion battery considering the uncertainty with quantile distribution of deep features Y Zhang, M Zhang, C Liu, Z Feng, Y Xu Reliability Engineering & System Safety 245, 110002, 2024 | 16 | 2024 |
| 基于多源信息融合的往复式压缩机故障诊断方法 张明， 江志农 机械工程学报 53 (23), 46-52, 2017 | 14 | 2017 |
| A novel simulated annealing-based hyper-heuristic algorithm for stochastic parallel disassembly line balancing in smart remanufacturing Y Hu, C Liu, M Zhang, Y Jia, Y Xu Sensors 23 (3), 1652, 2023 | 13 | 2023 |
| Domain adaptation with multilayer adversarial learning for fault diagnosis of gearbox under multiple operating conditions M Zhang, W Lu, J Yang, D Wang, L Bin 2019 Prognostics and System Health Management Conference (PHM-Qingdao), 1-6, 2019 | 12 | 2019 |
| A novel health indicator by dominant invariant subspace on Grassmann manifold for state of health assessment of lithium-ion battery Y Zhang, YF Li, M Zhang, H Wang Engineering Applications of Artificial Intelligence 130, 107698, 2024 | 11 | 2024 |
| Cost modelling to support optimum selection of life extension strategy for industrial equipment in smart manufacturing N Amaitik, M Zhang, Z Wang, Y Xu, G Thomson, Y Xiao, N Kolokas, ... Circular Economy and Sustainability 2 (4), 1425-1444, 2022 | 11 | 2022 |
| Research on intelligent manufacturing system based on multi-agent Q Guo, M Zhang Intelligent Robotics and Applications: First International Conference, ICIRA …, 2008 | 11 | 2008 |
| An early warning method for mechanical fault detection based on adversarial auto-encoders HOU Jijie, MA Bo, L Liang, M Zhang Journal of Advanced Manufacturing Science and Technology 2 (2), 2022 | 10 | 2022 |
| 基于动态自学习阈值和趋势滤波的机械故障智能预警方法 张明， 冯坤， 江志农 振动与冲击 33 (24), 8-14, 2014 | 10 | 2014 |
| An ontology and rule-based method for human–robot collaborative disassembly planning in smart remanufacturing Y Hu, C Liu, M Zhang, Y Lu, Y Jia, Y Xu Robotics and Computer-Integrated Manufacturing 89, 102766, 2024 | 8 | 2024 |
| Few-shot learning approaches for fault diagnosis using vibration data: a comprehensive review X Liang, M Zhang, G Feng, D Wang, Y Xu, F Gu Sustainability 15 (20), 14975, 2023 | 8 | 2023 |
| A distributional perspective on remaining useful life prediction with deep learning and quantile regression M Zhang, D Wang, N Amaitik, Y Xu IEEE Open Journal of Instrumentation and Measurement 1, 1-13, 2022 | 8 | 2022 |
Yuchun XuProfessor of Manufacturing, Aston UniversityEmail verificata su aston.ac.uk