My researchgate:  https://www.researchgate.net/profile/Ping-Wu-24/research

主持项目：

[P1] 2018.1-2020.12  国家自然科学青年基金: 基于子空间方法和散度测度的大型天然气液化装置微小故障检测与诊断研究（已结题）

[P2] 2019.1-2021.12  浙江省基础研究与公益项目：基于深度学习和物联网的异步电机故障诊断关键技术研究与系统开发（已结题）

[P3] 2016.1-2018.12 浙江省基础研究与公益项目：基于物联网的天然气场站压力控制回路监测与诊断（已结题）

[P4]  2020.1-2021.12   浙江大学工业控制技术国家重点实验室开放基金：基于深度偏最小二乘法的质量相关故障诊断方法及其在高炉冶炼过程的应用（已结题）

[P5] 2019.1-2020.12  江南大学轻工过程先进控制教育部重点实验室开放基金：基于核范数子空间辨识的质量相关故障检测方法研究（已结题）

[P6] 2020.1-2022.12  企业横向：电梯图像识别与控制 （在研）

[P7] 2022.1-2023.12   企业横向：流程行业压缩机与水泵数字孪生模块（在研）

[P8] 2022.1-2023.12   企业横向：变电站故障诊断智能监测 （在研）

[P9] 2022.1-2023.12   浙江省智能制造质量大数据溯源与应用重点实验室开放课题: 基于工业大数据驱动的轧钢过程质量监控与溯源研究 （在研）

[P10] 2023.1-2025.12  企业横向：基于工业大数据的特种纸制造优化运行技术（在研）

发表论文：

[J29] Ye, Z., Wu Ping, He, Y., Pan, H. SSAE‐KPLS: A quality‐related process monitoring via integrating stacked sparse autoencoder with kernel partial least squares. The Canadian Journal of Chemical Engineering, 2023, DOI: 10.1002/cjce.24886. 

[J28] Lou, S., Wu Ping, Yang, C., Xu, Y. Structured fault information-aided canonical variate analysis model for dynamic process monitoring. Journal of Process Control, 2023,124:54-69. 

[J27]X Wang, Wu Ping, Y Huo, X Zhang, Y Liu, L Wang.Data-driven fault detection of a 10 MW floating offshore wind turbine benchmark using kernel canonical variate analysis, Measurement Science and Technology, 2023, 34.

[J26] S Lou, C Yang, Wu Ping, L Kong, Y Xu. Fault Diagnosis of Blast Furnace Iron-Making Process With a Novel Deep Stationary Kernel Learning Support Vector Machine Approach, IEEE Transactions on Instrumentation and Measurement, 2022, 71, 1-13.

[J25] S Lou, C Yang, Wu Ping. A Local Dynamic Broad Kernel SSA for Monitoring Blast Furnace Ironmaking Process, IEEE Transactions on Industrial Informatics, 2022.

[J24] X Wang, Wu Ping. Nonlinear Dynamic Process Monitoring Based on Ensemble Kernel Canonical Variate Analysis and Bayesian Inference. ACS Omega, 2022.

[J23] H Ye, Wu Ping, Y Huo, X Wang, Y He, X Zhang, J Gao. Bearing Fault Diagnosis Based on Randomized Fisher Discriminant Analysis. Sensors, 2022, 22 (21), 8093

[J22] Zhang, X., Wu Ping, He, J., Liu, Y., Wang, L., & Gao, J. Floating offshore wind turbine fault diagnosis using stacked denoising autoencoder with temporal information. Transactions of the Institute of Measurement and Control, 2021, 01423312211057994.

[J21]  Tong, Y., Wu Ping, He, J., Zhang, X., & Zhao, X. Bearing fault diagnosis by combining a deep residual shrinkage network and bidirectional LSTM. Measurement Science and Technology, 2021, 33(3), 034001.

[J20] Jiajun He,Wu Ping, Yizhi Tong,Xujie Zhang,Meizhen Lei and Jinfeng Gao. Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN, Sensors 2021, 21(21), 7319.

[J19] Wu Ping, Yichao Liu, Riccardo M.G. Ferrari,Jan-Willem van Wingerden. Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis, IET Renewable Power Generation, 2021, doi.org/10.1049/rpg2.12319

[J18] Yichao Liu,  Joeri Frederik,  Riccardo M.G. Ferrari,  Wu Ping,  Sunwei Li,  Jan‐Willem van Wingerden. Fault‐tolerant individual pitch control of floating offshore wind turbines via subspace predictive repetitive control, Wind Energy, 2021, doi.org/10.1002/we.2616.

[J17] Wu Ping;  S. Lou;  X. Zhang;  J. He;  Y. Liu;  J. Gao; Data-Driven Fault Diagnosis Using Deep Canonical Variate Analysis and Fisher Discriminant Analysis, IEEE Transactions on Industrial Informatics, 2021, 17(5): 3324-3334.

[J16] Wu Ping;  Xujie Zhang;  Jiajun He;  Siwei Lou;  Jinfeng Gao; Locality preserving randomized canonical correlation analysis for real-time nonlinear process monitoring, Process Safety and Environmental Protection, 2021, 147: 1088-1100.    

[J15] Wu Ping;  Lou, Siwei;  Zhang, Xujie;  He, Jiajun;  Gao, Jinfeng; Novel Quality-Relevant Process Monitoring based on Dynamic Locally Linear Embedding Concurrent Canonical Correlation Analysis, Industrial & Engineering Chemistry Research, 2020, 59(49): 21439-21457.    

[J14] Wu Ping;  R. Ferrari;  Y. Liu;  J. -W. van Wingerden; Data-Driven Incipient Fault Detection via Canonical Variate Dissimilarity and Mixed Kernel Principal Component Analysis, IEEE Transactions on Industrial Informatics, 2020.   

[J13] Lingling Guo; Wu Ping;  Siwei Lou;  Jinfeng Gao;  Yichao Liu; A multi-feature extraction technique based on principal component analysis for nonlinear dynamic process monitoring, Journal of Process Control, 2020, 85: 159-172.

[J12] Lou, Siwei; Wu Ping;  Guo, Lingling;  He, Jiajun;  Zhang, Xujie;  Gao, Jinfeng; Dynamic process monitoring using dynamic latent-variable and canonical correlation analysis model, Canadian Journal of Chemical Engineering, 2020.

[J11] L. Guo;Wu Ping;  J. Gao;  S. Lou; Sparse Kernel Principal Component Analysis via Sequential Approach for Nonlinear Process Monitoring, IEEE Access, 2019, 7: 47550-47563.    

[J10] Wu Ping;  L. Guo;  S. Lou;  J. Gao; Local and Global Randomized Principal Component Analysis for Nonlinear Process Monitoring, IEEE Access, 2019, 7: 25547-25562.   

[J9] Yichao Liu; Riccardo Ferrari; Wu Ping;  Xiaoli Jiang;  Sunwei Li;  Jan-Willem van Wingerden; Fault diagnosis of the 10MW Floating Offshore Wind Turbine Benchmark: A mixed model and signal-based approach, Renewable Energy, 2021, 164: 391-406.  

[J8] Siwei Lou; Wu Ping;  Lingling Guo;  Yiyong Duan;  Xujie Zhang;  Jinfeng Gao; Sparse Principal Component Analysis Using Particle Swarm Optimization, Journal of Chemical Engineering of Japan, 2020, 53(7): 327-336.   

[J7] HaiYun Zhou; Ping Wu; A Comparison Study of Subspace Identification of Blast Furnace Ironmaking Process, Journal of Chemical Engineering of Japan, 2020, 53(9): 540-545.  

[J6] Siwei Lou; Wu Ping;  Lingling Guo;  Yiyong Duan;  Xujie Zhang;  Jinfeng Gao; Sparse Principal Component Analysis Using Particle Swarm Optimization, Journal of Chemical Engineering of Japan, 2020, 53(7): 327-336.   

[J5] Wu Ping;  Guo, Lingling;  Duan, Yiyong;  Zhou, Wei;  He, Guojun; Control loop performance monitoring based on weighted permutation entropy and control charts, Canadian Journal of Chemical Engineering, 2019, 97(S1): 1488-1495.

[J4] Wu Ping; Performance monitoring of MIMO control system using Kullback-Leibler divergence, Canadian Journal of Chemical Engineering, 2018, 96(7): 1559-1565.

[J3] Wu Ping;  Pan, HaiPeng;  Ren, Jia;  Yang, Chunjie; A New Subspace Identification Approach Based on Principal Component Analysis and Noise Estimation, Industrial & Engineering Chemistry Research, 2015, 54(18): 5106-5114. 

[J2] Wu Ping, CHEN Liang, ZHOU Wei, GUO Ling-ling; Online subspace identification based on principal component analysis and noise estimation, Journal of Zhejiang University(Engineering Science), 2018, 52(9): 1694.   

[J1] Shu Lin; Chunjie Yang; Wu Ping;  Zhihuan Song; Active surge control for variable speed axial compressors, ISA Transactions, 2014, 53(5): 1389-1395.    

[C5] X Wang, Wu Ping. Blast Furnace Ironmaking Process Fault Detection Using Canonical Variate Analysis and Support Vector Data Description. 2022, 34th Chinese Control and Decision Conference (CCDC).

[C4] X Wang, Wu Ping, S Lou. Quality-Relevant Process Monitoring Based on Improved Concurrent Canonical Correlation Analysis. 2021 IEEE 10th Data-Driven Control and Learning Systems Conference (DDCLS).

[C3] X. Zhang; Wu Ping;  J. He;  S. Lou;  J. Gao; A GAN Based Fault Detection of Wind Turbines Gearbox, 2020 7th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS).    

[C2] L. Guo; Wu Ping;  S. Lou;  J. Gao; Sparse dynamic inner principal component analysis for process monitoring, 2019 Chinese Automation Congress (CAC).   

[C1] S. Lou; Wu Ping;  L. Guo;  J. Gao; A Novel Structured Dynamic CCA Modeling for Process Monitoring, 2019 Chinese Automation Congress (CAC).