2010.08 -- 2014.07  哈尔滨工程大学  电气工程及其自动化  学士

2014.09 -- 2016.07  哈尔滨工业大学  控制科学与工程     硕士

2018.08 -- 2022.06  清华大学      控制科学与工程     博士

2016.07-2018.07  中科院海西研究院泉州装备制造研究所 

2022.08至今  东南大学自动化学院  讲师

中国自动化学会会员， IEEE会员

课题组隶属于魏海坤院长团队，主要研究方向包括故障检测与诊断、工业大数据处理、机器学习等及其在工业领域的应用，基于多源信息融合的光伏功率预测等。

课题组以科研为导向，学风严谨、指导详细，以人为本，结合学生的职业发展确定培养模式，欢迎有进取心、好奇心的学生报考，一定会全力支持学生的科研工作。

每年本部招收1名研究生，苏州校区1名研究生。

希望学生具有一定的机器学习、深度学习、数据处理等领域的知识或项目经历，初步掌握时间序列数据或图像数据的处理技巧，掌握matlab或python编程语言。

机器学习、深度学习、工业大数据处理、持续学习、故障检测与诊断、光伏发电功率预测

[1] 国家自然科学基金青年基金，超超临界机组非平稳运行过程故障早期检测与定位研究， 2024-2026，主持

[2] 江苏省自然科学基金青年基金，数据驱动的异常监测方法及其在大型发电机组的应用，2023-2026，在研，主持

[3] 国家电网科技部项目, 基于多源气象数据融合的短临天气过程辨识技术研究, 2023-10 至 2025-12, 在研, 主持

[4] 中央高校基本科研业务费专项，数据驱动的非平稳过程故障检测与定位研究，2023-2026，在研，主持

[5] 东南大学教育部重点实验室开放课题，光伏发电系统非平稳过程的故障早期检测研究，2023-2025，在研，主持

[6] 国家自然科学基金应急管理项目，大型发电机组异常工况智能预测与自愈控制研究，2018-2021，项目骨干

[7]  国家自然科学基金面上项目，大数据驱动的故障检测：改进的PCA与PLS方法 ，2018-2022，项目骨干    

[8] 国家自然科学基金重点项目，旋转导向钻井工具精确轨迹跟踪的智能自主容错控制系统研究，2020-2023，项目参与者

[1] Jingxin Zhang, Xiao, James, Maoyin Chen, Xia Hong. Multimodal continual learning for process monitoring: a novel weighted canonical correlation analysis with attention mechanism. IEEE Transactions on Neural Networks and Learning Systems, 2023, DOI:10.1109/TNNLS.2023.3331732.

[2] Jingxin Zhang, Donghua Zhou, Maoyin Chen. Adaptive cointegration analysis and modified RPCA with continual learning ability for monitoring multimode nonstationary processes. IEEE Transactions on Cybernetics, 2023, 53(8):4841-4854. (regular paper)

[3] Jingxin Zhang, Donghua Zhou, Maoyin Chen. Self-learning sparse PCA for multimode process monitoring. IEEE Transactions on Industrial Informatics, 2023, 19(1): 29-39. (regular paper)

[4] Jingxin Zhang, Maoyin Chen, Xia Hong. Monitoring multimode nonlinear dynamic processes: an efficient sparse dynamic approach with continual learning ability. IEEE Transactions on Industrial Informatics, 2023, 19(7):8029-8038. (regular paper)

[5] Jingxin Zhang, Donghua Zhou, Maoyin Chen, Xia Hong. Continual learning for multimode dynamic process monitoring with applications to an ultra-supercritical thermal power plant. IEEE Transactions on Automation Science and Engineering, 2023, 20(1):137-150. (regular paper)

[6] Jingxin Zhang, Donghua Zhou, Maoyin Chen, Xia Hong. Continual learning-based probabilistic slow feature analysis for monitoring multimode nonstationary processes. IEEE Transactions on Automation Science and Engineering, 2024, 21(1):733-745. (regular paper)

[7] 张景欣, 周东华, 陈茂银, 吴德浩. 数据驱动的多工况过程异常监测方法：综述与展望. 中国科学: 信息科学, 2023, 53(11): 5579-5587. (综述)

[8]  Jingxin Zhang, Donghua Zhou, Maoyin Chen. Multimode process monitoring: a modified PCA algorithm with continual learning ability. Journal of Process Control, 2021, 103:76-86. (regular paper)

[9]  Jingxin Zhang, Hao Chen, Songhang Chen, Xia Hong. An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring. IEEE Transactions on Cybernetics, 2019, 49(1): 198-210. (regular paper)

[10] Jingxin Zhang, Maoyin Chen, Xia Hong. Nonlinear process monitoring using a mixture of probabilistic PCA with clusterings. Neurocomputing, 2021, 458: 319-326. (brief paper)

[11] Jingxin Zhang, Maoyin Chen, Hao Chen, Xia Hong, Donghua Zhou. Process monitoring based on orthogonal locality preserving projection. Industrial & Engineering Chemistry Research, 2019, 58(14): 5579-5587. (regular paper)

[12] Jinhua Guo, Hao Chen, Jingxin Zhang, Sheng Chen. Structure parameter optimized kernel based online prediction with a generalized optimization strategy for nonstationary time series. IEEE Transactions on Signal Processing, 2022, 70: 2698-2712. (regular paper)

[13] Dehao Wu, Donghua Zhou, Jingxin Zhang, Maoyin Chen. Multimode process monitoring based on fault dependent variable selection and moving window-negative log likelihood probability. Computers & Chemical Engineering, 2020, 136: 106787. (regular paper)

[14] Shen Yin, Chengming Yang, Jingxin Zhang, Yuchen Jiang. A data-driven learning approach for nonlinear process monitoring based on available sensing measurements. IEEE Transactions on Industrial Electronics, 2017, 64(1): 643-653. (regular paper)