王永健，山东莱州人，东南大学副研究员、硕士生导师、洪堡学者。围绕聚焦故障诊断领域，进行从故障检测与诊断、故障溯源、故障预测、故障可视化、容错控制及自愈到性能评估等的闭环研究，将机理模型、机器学习等数据驱动模型以及工艺知识相融合，不断丰富工业大数据建模的可解释性等工作。主要面向流程工业与机械、机电系统的信号处理。致力于理论结合实际。发表SCI论文30余篇，EI论文若干；曾获校优秀博士学位论文、香港博士后奖学金及德国洪堡学者基金等。主持江苏省自然科学基金，参与多项国家自然科学基金项目及企业合作项目。为IEEE Trans, JPC, CEP, IECR 等多个期刊审稿人。IEEE Member, 江苏省自动化学会普及与咨询工作委员会副主任兼秘书长。

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教育背景

2011年9月 - 2015年6月北京化工大学本科

2015年9月 - 2020年11月北京化工大学&美国加州大学洛杉矶分校（硕博连读）

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工作经历

2022年1月 - 至今东南大学，自动化学院，副研究员

2022 年8月– 2024年7月，德国杜伊斯堡-埃森大学，洪堡学者（导师：Steven X. Ding）

2021年1月 - 2021年12月香港城市大学数据科学学院（导师：秦泗钊, Fellow of IEEE, AIChE, IFAC, NAI）

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学术兼职

中国人工智能学会、中国电子教育学会、江苏省自动化学会普及与咨询工作委员会

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讲授课程

《控制系统建模与分析综合设计Ⅱ》、《复杂系统与过程控制》（全英文）

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研究兴趣及学生培养

从兴趣出发引导，将理论与实际相结合。

围绕聚焦故障诊断领域，进行从故障检测与诊断、故障溯源、故障预测、故障可视化、容错控制及自愈到性能评估等的闭环研究，将机理模型、机器学习等数据驱动模型以及工艺知识相融合，不断丰富工业大数据建模的可解释性等工作。主要面向流程工业与机械、机电系统的信号处理。

每年招收四牌楼校区硕士研究生2名，苏州或无锡校区硕士研究生1名。

感兴趣的可以联系我，我们共同成长O(∩_∩)O。

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科研项目

育部重点实验室基金，在研，主持

新进教师科研启动基金，在研，主持

江苏省自然科学基金，在研，主持

三维桥式智能转载教学训练系统，在研，横向，主持

算力统筹调度平台的数据安全，在研，横向，主持

国家自然科学基金面上项目, 在研, 参与

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论文发表

1.       Wang Y, Bao D, Qin S J. A novel bidirectional DiPLS based LSTM algorithm and its application in industrial process time series prediction[J]. Chemometrics and Intelligent Laboratory Systems, 2023, 240: 104878.

2.       Wang Y, Li S, Chen X, et al. A novel operational modality classification method based on image joint contrast[J]. Chemical Engineering Science, 2023, 277: 118864.

3.       Luo Y, Gopaluni B, Cao L, Wang Y, et al. Adaptive online optimization of alarm thresholds using multilayer Bayesian networks and active transfer entropy[J]. Control Engineering Practice, 2023, 137: 105534.

4.       Wang Y, Qian C, Qin S J. Attention-mechanism based DiPLS-LSTM and its application in industrial process time series big data prediction[J]. Computers & Chemical Engineering, 2023, 176: 108296.

5.       Bao D, Li S, Wang Y*. Optimized utilization and interpretability of process data with data-driven model[C]//2023 6th International Symposium on Autonomous Systems (ISAS). IEEE, 2023: 1-5.

6.       Wang Z, Yang F, Xu Q, Wang Y*, et al. Capacity estimation of lithium-ion batteries based on data aggregation and feature fusion via graph neural network[J]. Applied Energy, 2023, 336: 120808.

7.       Li M, Zheng K, Zhao Y, Wang Y, et al. LSTnet-GRU-A: Traffic Flow Forecasting Based on Attention Mechanism[C]//2022 China Automation Congress (CAC). IEEE, 2022: 6487-6492.

8.       Qian C, Wang Y*, Li S. A Novel CVA Fault Detection Method Based on Abnormal Data Elimination[C]//2022 China Automation Congress (CAC). IEEE, 2022: 2409-2414.

9.       Wang Y, Yu Z, Wang Z. A temporal clustering method fusing deep convolutional autoencoders and dimensionality reduction methods and its application in air quality visualization[J]. Chemometrics and Intelligent Laboratory Systems, 2022, 227: 104607.

10.    Qi C, Zeng X, Wang Y*, et al. Adaptive time window convolutional neural networks concerning multiple operation modes with applications in energy efficiency predictions[J]. Energy, 2022, 240: 122506.

11.    Wang Y, Yang K, Li H. Industrial time-series modeling via adapted receptive field temporal convolution networks integrating regularly updated multi-region operations based on PCA[J]. Chemical Engineering Science, 2020, 228: 115956. (领域TOP)

12.    Wang Y, Zhang Y, Wu Z, et al. Operational trend prediction and classification for chemical processes: A novel convolutional neural network method based on symbolic hierarchical clustering[J]. Chemical Engineering Science, 2020, 225: 115796. (领域TOP)

13.    Wang Y, Li H, Yang B. Modeling of furnace operation with a new adaptive data echo state network method integrating block recursive partial least squares[J]. Applied Thermal Engineering, 2020, 171: 115088.(领域TOP)

14.    Wang Y, Huang J, Su C, et al. Furnace thermal efficiency modeling using an improved convolution neural network based on parameter-adaptive mnemonic enhancement optimization[J]. Applied Thermal Engineering, 2019, 149: 332-343. (领域TOP)

15.    Wang Y, Li H. Industrial process time-series modeling based on adapted receptive field temporal convolution networks concerning multi-region operations[J]. Computers & Chemical Engineering, 2020, 139: 106877. (领域TOP)

16.    Wang Y, Ren Y M, Li H. Symbolic Multivariable Hierarchical Clustering Based Convolutional Neural Networks with Applications in Industrial Process Operating Trend Predictions[J]. Industrial & Engineering Chemistry Research, 2020, 59(34): 15133-15145. (领域TOP)

17.    Wang Y, Zhang Y, Li H. Adapted Receptive Field Temporal Convolutional Networks with Bar-Shaped Structures Tailored to Industrial Process Operation Models[J]. Industrial & Engineering Chemistry Research, 2020, 59(13): 5482-5490. (领域TOP)

18.    Wang Y, Li H. Complex Chemical Process Evaluation Methods Using a New Analytic Hierarchy Process Model Integrating Deep Residual Network with Multiway Principal Component Analysis[J]. Industrial & Engineering Chemistry Research, 2019, 58(31): 13889-13899. (领域TOP)

19.    Wang Y, Li H, Huang J, et al. An improved bar-shaped sliding window CNN tailored to industrial process historical data with applications in chemical operational optimizations[J]. Industrial & Engineering Chemistry Research, 2019, 58(47): 21219-21232. (领域TOP)

20.    Wang Y, Li H, Qi C. An adaptive mode convolutional neural network based on bar-shaped structures and its operation modeling to complex industrial processes[J]. Chemometrics and Intelligent Laboratory Systems, 2020, 199: 103932. (JCR 1区)

21.    Wang Y, Li H. Complex chemical process operation evaluations using a novel analytic hierarchy process model integrating deep residual network with principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, 2019, 191: 118-128. (JCR 1区)

22.    Wang Y, Li H. A novel intelligent modeling framework integrating convolutional neural network with an adaptive time-series window and its application to industrial process operational optimization[J]. Chemometrics and Intelligent Laboratory Systems, 2018, 179: 64-72.(JCR 1区)

23.    Li J, Li H, Wang Y, et al. Hybrid cycle reservoir with jumps for multivariate time series prediction: An industrial application in oil drilling process[J]. Measurement Science and Technology, 2019.

24.    Li J, Yang B, Li H, Wang Y, et al. DTDR–ALSTM: Extracting dynamic time-delays to reconstruct multivariate data for improving attention-based LSTM industrial time series prediction models[J]. Knowledge-Based Systems, 2021, 211: 106508. (JCR 1 区)

25.    Li J, Wang Y, Li H, et al. An Adaptive Optimization Approach Based on the Human factors and Its Application to Process Alarm Thresholds[C]//2019 12th Asian Control Conference (ASCC). IEEE, 2019: 823-828.

26.    Yang K, Wang Y, Yao Y, et al. Remaining useful life prediction via long‐short time memory neural network with novel partial least squares and genetic algorithm[J]. Quality and Reliability Engineering International, 2020, 37 (3), 1080-1098.

27.    Ren K, Wang Y, Yang B, et al. A Novel Multivariable Nonlinear Time Series Prediction Method for APSO-Elman Network[C]//2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2019: 218-224.

28.    Qiao L, Li H, Wang Y. Nonlinear Dynamic System Predictions Based on Neural Networks with Fruit Fly Optimization Algorithms[C]//2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2019: 123-128.

29.    Tian S, Li H, Wang Y. Brain Emotional Learning Networks with Applications[C]//2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2019: 37-41.

30.    Ren Y M, Zhang Y, Ding Y, Wang Y, et al. Computational fluid dynamics-based in-situ sensor analytics of direct metal laser solidification process using machine learning[J]. Computers & Chemical Engineering, 2020, 143: 107069. (领域TOP)

31.    Yang K, Wang Y, Fan S, Ali M. Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method[J]. Applied Science, 2021, 11(15), 7088

32.    朱坚，杨博，王永健，唐晓婕，李宏光. 一种新型的基于Levenshtein距离层次聚类的时序操作优化方法. 化工学报，2019, 70(2): 581-589.