张恒
副研究员
专业：控制科学与工程
所属科研团队：装备智能健康评估
研究方向：装备故障预测与健康管理，剩余寿命预测，电池管理系统
Email: hengscu@163.com
地址：四川省成都市一环路南一段24号四川大学基础教学楼B座217
学习和工作简历
教育经历：
2016/09-2021/06，四川大学空天科学与工程学院，机械工程，博士，导师：苗强教授
2012/09-2016/06，哈尔滨工程大学航天与建筑工程学院，质量与可靠性工程，学士
访学经历：
2019/12-2020/11，University of South Carolina，美国，联合培养博士，导师：Bin Zhang 教授
工作经历：
2021/06-至今，四川大学，电气工程学院，副研究员（专职科研）
2021/09-2023/06，四川大学，电气工程学院，助理研究员
科研项目：
[1] 国家自然科学基金，基于Lebesgue采样的动力电池组状态联合估计与寿命预测研究，主持
[2] 四川省科技厅，飞行器机载电传作动系统健康状态在线评估与预测研究，主持
[3] 博士后基金，资源受限的空间锂离子蓄电池组在轨健康管理关键技术研究，主持
[4] 国家重点研发计划项目，大规模制造产业网状结构价值链数字生态理论研究，主研
[5] 国家自然科学基金，微重力环境下低速空间旋转机构动态服役行为表征与评估，主研
代表性学术成果：
[1] Lyu G, Zhang H, Miao Q. Parallel State Fusion LSTM-based Early-cycle Stage Lithium-ion Battery RUL Prediction Under Lebesgue Sampling Framework. Reliability Engineering & System Safety, 2023, 236: 109315.
[2] Lyu G, Zhang H, Miao Q. RUL Prediction of Lithium-Ion Battery in Early-Cycle Stage Based on Similar Sample Fusion Under Lebesgue Sampling Framework. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-11.
[3] Miao J, Deng C, Zhang H, et al. Interactive channel attention for rotating component fault detection with strong noise and limited data. Applied Soft Computing, 2023, 138: 110171.
[4] Lyu G, Zhang H, Zhang Y J, et al. An interpretable remaining useful life prediction scheme of lithium-ion battery considering capacity regeneration. Microelectronics Reliability, 2022: 114625.
[5] Wang J, Zeng Z, Zhang H, et al. An Hybrid Domain Adaptation Diagnostic Network Guided by Curriculum Pseudo Labels for Electro-mechanical Actuator. Reliability Engineering & System Safety, 2022: 108770.
[6] Yan X, H. Zhang, Luo C, et al. Degree of Cyclic Target Protrusion Defined on Squared Envelope Spectrum for Rotating Machinery Fault Diagnosis. Measurement, 2021: 110634.
[7] H. Zhang, G.X. Niu, B. Zhang, and Q. Miao, “Cost-Effective Lebesgue Sampling Long Short-Term Memory Networks for Lithium-Ion Batteries Diagnosis and Prognosis,” IEEE Transactions on Industrial Electronics, 2022, Vol.69, No.2, pp.1958-1967.
[8] H. Zhang, E. Liu, B. Zhang, and Q. Miao, “RUL prediction and uncertainty management for multisensor system using an integrated data-level fusion and UPF approach,” IEEE Transactions on Industrial Informatics, vol. 17, no. 7, pp. 4692 - 4701, 2021.
[9] H. Zhang, Z. Mo, J. Wang, and Q. Miao, “Nonlinear-drifted fractional Brownian motion with multiple hidden state variables for remaining useful life prediction of lithium-ion batteries,” IEEE Transactions on Reliability, vol. 69, no. 2, pp. 768–780, 2019.
[10] H. Zhang, Miao Q, Zhang X, and Liu ZW, “An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction,” Microelectronics Reliability, 2018, 81: 288-298.
[11] Z.L. Mo, H. Zhang, J.L. Wang, J.Y. Wang, H.Y. Fu, and Q. Miao, “Adaptive Meyer wavelet filters for machinery fault diagnosis based on harmonic infinite-taxicab norm and grasshopper optimization algorithm,” Proceedings of the iMeche, Part C: Journal of Mechanical Engineering Science, 2021, Vol.235, No.19, pp.4458-4474.
[12] Z.L. Mo, J.Y. Wang, H. Zhang, and Q. Miao, “Weighted cyclic harmonic-to-noise ratio for rolling element bearing fault diagnosis,” IEEE Transactions on Instrumentation and Measurement, 2020, Vol.69, No.2, pp.432-442.
[13] J.Y. Wang, Z.L. Mo, H. Zhang, and Q. Miao, “Ensemble diagnosis method based on transfer learning and incremental learning towards mechanical big data,” Measurement, 2020, Vol. 155, 107517.
[14] 苗强, 蒋京, 张恒, 罗冲. 工业大数据背景下的航空智能发动机：机遇与挑战, 仪器仪表学报, 2019, Vol.40, No.7, pp. 1-12.
人才培养：
研究生选修课程：《检测技术与自动化》、《可靠性系统工程》、《运筹学》
学术兼职：
航空学会PHM分会青年委员，IEEE会员，担任IEEE Transactions on Power Systems，IEEE Transactions on Industrial Informatics，IEEE Transactions on Industrial Electronics，IEEE Transactions on Instrumentation and Measurement等多个期刊审稿人。
