个人简介

孔金震，工学博士，河北工业大学机械工程学院准聘副教授，系副主任，硕导，河北工业大学启航B岗、“兴冀青年拔尖人才”、天津市企业科技特派员。2023年于上海交通大学获得博士学位，长期从事设备故障预测及健康管理、动力电池健康状态评估及寿命预测理论与方法、智能运维与大数据分析等研究，主持国家自然科学基金青年基金、天津市科研项目、校企合作项目等，作为学术骨干参与包括国家重点研发计划、国家自然科学基金创新群体项目、国家自然科学基金等多项国家级课题。发表SCI论文10余篇，出版英文专著1章，获批国家发明专利授权2项。担任Mechanical Systems and Signal Processing、IEEE Transactions on Transportation Electrification、IEEE Sensors Journal等10余个国际学术期刊审稿人。

教育背景

2017.09-2023.06  上海交通大学    机械工程(工业工程)  博士(硕博连读)  导师：彭志科、王冬

2023.01-2023.05  新南威尔士大学  机械与制造工程  访问学者  合作导师：Zhongxiao Peng

2021.08-2022.01  新加坡国立大学  工业系统工程与管理  访问学者  合作导师：Zhisheng Ye

2013.08-2017.06  哈尔滨工程大学  机械设计制造及其自动化  学士

工作经历

2025.01-至今     河北工业大学机械工程学院，测控技术与仪器系，准聘副教授，系副主任

2023.07-2024.12  河北工业大学机械工程学院，测控技术与仪器系，讲师

2023.07-至今     河北工业大学机械工程学院，测控224班，班导师

研究方向

动力电池寿命预测理论与方法

设备智能运维与大数据分析

退化建模数学优化模型研究

设备故障预测与健康管理 (PHM)

科研项目

主要科研成果

学术论文：

[1] Kong Jin-Zhen, Cui Di, Hou Bingchang, et al., New Short-long-term Degradation Model for Precise Battery Health Prognostics, IEEE Transactions on Industrial Electronics, 2023, 70(9): 9527-9537. (SCI，Q1，一区TOP)

[2] Kong Jin-Zhen, Yang Fangfang, Zhang Xi, et al., Voltage-temperature Health Feature Extraction to Improve Prognostics and Health Management of Lithium-ion Batteries, Energy, 2021, 223: 120114. (SCI，Q1，一区TOP)

[3] Kong Jin-Zhen, Wang Dong, Yan Tongtong, et al., Accelerated Stress Factors Based Nonlinear Wiener Process Model for Lithium-ion Battery Prognostics, IEEE Transactions on Industrial Electronics, 2021, 69(11): 11665-11674. (SCI，Q1，一区TOP)

[4] Kong Jinzhen, Liu Jie, Zhu Jingzhe, Zhang Xi, Tsui Kwok-Leung, Peng Zhike, Wang Dong*; Review on Lithium-ion Battery PHM from the Perspective of Key PHM Steps, Chinese Journal of Mechanical Engineering, 2024, 37(1): 71. (期刊封面文章，SCI，Q1，二区)

[5] Kong Jin-Zhen, Liu Jie, Chen Yikai, et al., A Data-driven Approach for Capacity Estimation of Batteries Based on Voltage Dependent Health Indicators. Journal of Physics: Conference Series, 2021, 1983(1): 012115. (EI)

[6] Zhen Dong, Liu Jiahao, Ma Shuqin, Zhu Jingyu, Kong Jinzhen*, Gao Yizhao, Feng Guojin, Gu Fengshou, Online battery model parameters identification approach based on bias-compensated forgetting factor recursive least squares, Green Energy and Intelligent Transportation, 2024, 100207. (EI, ESCI)

[7] Wang Dong, Kong Jin-Zhen, Zhao Yang et al., Piecewise Model Based Intelligent Prognostics for State of Health Prediction of Rechargeable Batteries with Capacity Regeneration Phenomena, Measurement, 2019, 147: 106836. (SCI，Q2，二区)

[8] Wang Dong, Kong Jin-Zhen, Yang Fangfang et al., Battery Prognostics at Different Operating Conditions, Measurement, 2020, 151: 107182. (SCI，Q2，二区)

[9] Liu Jie, Wang Dong, Kong Jinzhen, Li Naipeng, Peng Zhike, Tsui Kwok-Leung et al., New Look at Bayesian Prognostic Methods, IEEE Transactions on Automation Science and Engineering, 2024, Accepted. (SCI，Q1，二区TOP)

[10] Hou Bingchang, Feng Xiao, Kong Jin-Zhen et al., Optimized weights spectrum autocorrelation: A new and promising method for fault characteristic frequency identification for rotating Machine fault diagnosis, Mechanical Systems and Signal Processing, 2023, 191: 110200. (SCI，Q1，一区TOP)

[11] Hou Bingchang, Wang Dong, Kong Jin-Zhen et al., Understanding importance of positive and negative signs of optimized weights used in the sum of weighted normalized Fourier spectrum/envelope spectrum for machine condition monitoring, Mechanical Systems and Signal Processing, 2022, 174: 109094. (SCI，Q1，一区TOP)

[12] Yan Tongtong, Wang Dong, Kong Jin-Zhen et al., Definition of Signal-to-Noise Ratio of Health Indicators and Its Analytic Optimization for Machine Performance Degradation Assessment, IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-16. (SCI，Q1，二区)

英文专著：

[1] Kong Jin-Zhen, Wang Dong, Two Statistical Degradation Models of Batteries under Different Operating Conditions, Artificial Intelligence, Big Data and Data Science in Statistics: Challenges and Solutions in Environmetrics, the Natural Sciences and Technology. Cham: Springer International Publishing, 2022: 269-282. (英文专著章节)

国际会议：

[1] Kong Jin-Zhen, Wang Dong, Multi-Stage Modeling and Remaining Charge-Discharge Cycles Prediction of Rechargeable Batteries Considering Capacity Regeneration Phenomena, 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference, 2020. (国际会议)

[2] Liu Jiahao, Kong Jinzhen, Zhen Dong, et al., Online Parameter Identification of Lithium Battery Model Based on Bias Compensated Least Square, International conference on the Efficiency and Performance Engineering Network, 2023. (国际会议)

[3] Yan Tongtong, Kong Jin-Zhen, Wei Sha et al., Generic Framework for Integration of Spectral fusion with Optimization Modelling for Machine Performance Degradation Assessment, 14th International Conference on Damage Assessment of Structures, 2021. (国际会议，获得Best Paper Award)

发明专利/软著：

[1]     孔金震, 郭舒睿, 马淑芹, 甄冬, 冯国金, 张浩, 梁小夏，一种采用OCR方法的车身数字钢码识别方法，公开号：CN118711193A，公开日：2024.09.27

[2]     王冬, 孔金震, 侯炳昌, 刘洁, 朱景哲，基于温度加速因子的锂离子电池剩余寿命预测方法及系统，公开号：CN113761751A，公开日：2021.12.07.

[3] 王冬, 孔金震, 彭志科，基于健康因子提取的电池健康状况预测方法、系统及介质，授权号：CN113030744B，授权日：2022.06.28.

[4] 王冬, 孔金震, 王玉婷, 刘洁, 冯潇, 陆明, 张彬, 一种光纤端口发光功率衰减趋势预测方法，公开号：CN115545318A，公开日：2022.12.30.

[5] 王冬, 侯炳昌, 孔金震, 彭志科，一种基于优化故障特征频谱的机械故障诊断与状态监测方法，授权号：CN113639985B，授权日：2022.04.12.

[6] 王冬, 林思源, 孔金震, 舒心, 仝雪婷电池剩余寿命的预测方法、装置、电子设备及存储介质，公开号：CN118818351A，公开日：2024.10.22.

[7] 冯国金, 刘佳皓, 孔金震, 甄冬, 孟召宗, 冯宇兴，偏差补偿的遗忘递推最小二乘法的电池模型参数辨识方法，公开号：CN117665586A，公开日：2024.03.08.

[8] 智能立体库故障预测软件V1.0，2024SR1278111.

所获荣誉

[1] 河北工业大学2024年度优秀班导师、河北工业大学“十佳班导师提名”

[2] 上海市优秀毕业生、黑龙江省三好学生

[3] 第十六届“东风日产杯”全国工业工程应用案例大赛一等奖、第五届上海市工程管理创新大赛一等奖、首届全球应用算法实践典范大赛BPAA全场大奖、第十七届“华为杯”中国研究生数学建模竞赛二等奖等

学术兼职

[1] 中国振动工程学会会员，仪器仪表学会会员、中国运筹学会会员

[2] Session Chair of The International Workshop on Fault Diagnostics and Prognostics (TEPEN2024-IWFDP)

[3] Mechanical Systems and Signal Processing、IEEE Transactions on Transportation Electrification、IEEE Sensors Journal、IEEE Transactions on Instrumentation & Measurement、Green Energy and Intelligent Transportation等国际顶级学术期刊和行业主流期刊审稿人

招生信息

欢迎具有测控/机械/车辆/电气/控制等背景的同学报考，欢迎对科研感兴趣的本科生进组科研。智能检测与诊断研究团队提供良好的学习软硬件条件、科研津贴/奖励等，欢迎有理想抱负和责任心、热爱科研、有奋斗精神的同学加入！

联系方式

jinzhenkong@hebut.edu.cn