学      位：博士

所在系所：车辆工程系

办公地点：土木楼1013

电子邮箱：ying.zhang@ustb.edu.cn

科研方向：机电系统故障诊断与智能运维、动力电池健康管理、多源大数据分析

 

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社会/学术兼职：

中国机械工程学会设备智能运维分会青年委员

教育经历：

2010.08－2014.06 北京化工大学，安全工程，获学士学位

2014.09－2017.06 北京化工大学，安全科学与工程，获硕士学位

2017.08－2021.04 悉尼科技大学，机电工程，获博士学位

 

工作经历:

2021.05-2023.05 清华大学，工业工程系，博士后/助理研究员

2023.10-现在   北京科技大学，副教授

 

代表性论著：

[1] Zhang, Y., Gao, A., Yu, Z., Gao, T. and Feng, Z., 2026. Stator current AM-FM model and signature analysis for induction motor eccentricity detection. Mechanical Systems and Signal Processing, 242, p.113630. 

[2] Zhang, Y., Ma, H., Feng, Z., Lin, R. and Liang, M., 2025. Generator Stator Current Signal Analytical Models and Signature Analysis for Fault Diagnosis of Wind Turbine Planet Bearings. IEEE Transactions on Instrumentation and Measurement. 

[3] Zhang, Y., Li, Y.F., Zhang, M., and Wang, H., 2024. A Novel Health Indicator Constructed by Dominant Invariant Subspace on Grassmann Manifold for SoH of Lithium-ion Battery. Reliability Engineering and System Safety, 130, p.107698. 

[4] Zhang, Y., Zhang, M., Liu C., Feng Z. and Xu Y., 2024. Reliability Enhancement of State of Health Assessment Model of Lithium-ion Battery Considering the Uncertainty with Quantile Distribution of Deep Features. Reliability Engineering and System Safety, 245(3), 110002. 

[5] Zhang, Y., Gao, K., Ma, T., Wang, H., and Li, Y., 2024. Intelligent recognition of structural health state of EV lithium-ion Battery using transfer learning based on X-ray computed tomography. Reliability Engineering and System Safety, p. 110374, https://doi.org/10.1016/j.ress.2024.110374. 

[6] Zhang, Y. , Liu, S., Feng, Z., and Yang, W.,2026. Cross-Condition Transfer Learning With Ensemble-Kernel Domain Adaptation for Planetary Gearbox Fault Diagnosis Under Variable-Speed Conditions, IEEE Systems Journal, doi: 10.1109/JSYST.2026.3735512. 

[7] 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.

[8] 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.

[9] 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. 

[10] 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. 

[11] 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. 

[12] Yikun, E., Feng, Z., Liu, Z., Chen, H., Zhang, Y.*, Gao, T. and Lin, R., 2026. Dynamic modeling and vibration analysis for cycloid gear bearing fault diagnosis of rotate vector reducers. Mechanical Systems and Signal Processing, 244, p.113748. 

[13] Yikun, E., Feng, Z., Chen, X., Chen, H., Zhang, Y.*, Gao, T. and Lin, R., 2026. Electromechanical coupling dynamic modeling and motor current analysis for fault diagnosis of rotate vector reducers. Mechanical Systems and Signal Processing, 246, p.113903.

[14] Feng, Z., Li, Z., Lin, R., Gao, T., Zhang, Y.*, 2026. Vibration signal AM-FM models and spectral characteristics for rotate vector reducer fault diagnosis: an insight into multiple modulations and fault feature analysis. Mechanical Systems and Signal Processing, 261, p.115080.

[15] Feng, Z., Gao, T., E Y., Chen, X., Zhang, Y.*, 2025. Motor current analytical models and signature analysis for rotate vector reducer fault diagnosis. Mechanical Systems and Signal Processing.

[16] Feng, Z., Gao, T., Yu, X.*, Zhang, Y.*, Chen, X., Yang, Y. and Du, M., 2025. Planet bearing fault diagnosis via double encoder signal analysis. Mechanical Systems and Signal Processing, 224, p.111978. 

[17] Feng, Z., Lin, R., Zhang, Y.* and Lin, Z., 2025. Vibration signal analytical models and signature analysis for harmonic drive fault diagnosis. Mechanical Systems and Signal Processing, 241, p.113419. 

[18] Feng, Z., Lin R., Zhang Y.* and Lin Z., 2025. Vibration signal analytical model and signature analysis for harmonic drive flexible bearing fault diagnosis, Mechanical Systems and Signal Processing, 237, p. 112877. 

[19] Yikun, E., Liu, Z., Chen, H., Zhang, Y., Li, Z., Feng, H. and Feng, Z., 2025. Dynamic modeling and vibration analysis for fault diagnosis of rotate vector reducers. Mechanical Systems and Signal Processing, 224, p.111965.

[20] Zhu, N., Wang, J., Zhang, Y., Wang, H. and Han, T., 2024. An Adversarial Training Framework Based on Unsupervised Feature Reconstruction Constraints for Crystalline Silicon Solar Cells Anomaly Detection. IEEE Transactions on Instrumentation and Measurement.

主要科研项目：主持国家自然科学基金青年项目(52605113)、北京市自然科学基金面上项目(3262007)和广东省基础与应用基础研究基金项目(2024A1515110172)

 

成果与荣誉：获中国质量协会质量技术奖和中国发明协会发明创业奖，第27届“摇篮杯”大学生创新创业竞赛优秀指导教师