车辆工程系
ying.zhang@ustb.edu.cn
土木楼1013
社会/学术兼职:Mechanical Systems and Signal Processing, IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Industrial Informatics, IEEE Transactions on Industrial Electronics等期刊审稿人。
教育经历:
2010.08-2014.06 北京化工大学,机电工程学院,获学士学位
2014.09-2017.07 北京化工大学,机电工程学院,获硕士学位
2017.08-2021.04 悉尼科技大学,yl23455永利官网,获博士学位
工作经历:
2021.05-2023.09 清华大学,工业工程系,博士后
2023.10-至今 ayl23455永利官网,yl23455永利官网,特聘副教授
代表性论著:
(1)论文
[1] Zhang, Y. and Li, Y.F., 2022. Prognostics and health management of Lithium-ion battery using deep learning methods: A review. Renewable and Sustainable Energy Reviews, 161, p.112282.
[2] Zhang,Y., Li, Y.F., Zhang, M., and Wang, H., 2024. A novel health indicator by dominant invariant subspace on Grassmann manifold for state of health assessment of lithium-ion battery, Engineering Applications of Artificial Intelligence, 130, 107698.
[3] Zhang,Y., Zhang, M., Liu,C., Feng, Z.P. and Xu,Y.C., 2024. Reliability enhancement of state of health assessment model of lithium-ion battery considering the uncertainty with quantile distribution of deep features, Reliability Engineering & System Safety, 245, 110002.
[4] Zhang, Y. and Zhang, L., 2022. Intelligent fault detection of reciprocating compressor using a novel discrete state space. Mechanical Systems and Signal Processing, 169, p.108583.
[5] Zhang, Y. and Ji, J., 2020. Intelligent fault diagnosis of a reciprocating compressor using mode isolation convolutional deep belief networks. IEEE/ASME Transactions on Mechatronics, 26(3), pp.1668-1677.
[6] Zhang, Y., Ji, J. and Ma, B., 2020. Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network. Measurement, vol.156, p.107619.
[7] Zhang Y., Ji, J. and Ma, B., 2020. Reciprocating compressor fault diagnosis using an optimized convolutional deep belief network. Journal of Vibration and Control, p.1077546319900115.
[8] 马波, 张颖 and 于雷, 2018. 往复压缩机相空间 LDA模型在异常检测中的应用.机械设计与制造, (5), pp.12-15.
[9]张颖, 马波, 张明, 杨鲁伟and 杨俊玲, 2015. 基于 EMD 和 PCA 的滚动轴承故障信号特征提取研究.机电工程, 32(10), pp.1284-1289.
(2)专利
[1] 基于狄利克雷混合模型的转动机械运行状态异常检测方法. 授权号: ZL201610751063X.
[2] 一种智能电表的异常检测方法和系统. 授权号:ZL202310790273.X.
[3] 一种流程图读取方法及装置、电子设备和存储介质. 授权号:ZL 202310084775.0.
成果与荣誉:
2017年 国家留学基金委公派出国留学
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