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，ylzzcom永利总站线路检测，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  aylzzcom永利总站线路检测，ylzzcom永利总站线路检测，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