个人简述:

主要经历

2015年3月毕业于华东理工大学，获控制科学与工程专业博士学位。曾于博士研究生期间(2012年9月至2014年8月)，前往美国加州大学戴维斯分校（University Of California, Davis）过程系统工程实验室做访问学者。2015年4月起任职于宁波大学信息科学与工程学院。

研究方向

近年来主要从事数据驱动的工业过程故障检测与诊断方法研究，致力于将模式识别和大数据研究领域常用的数据建模、分析处理方法与技术应用于解决现代工业过程监测问题；此外，还从事与流程工业能耗问题相关的优化调度问题的研究、智能电网的最优设计与优化调度问题研究

招生情况

硕士生：计算机应用技术、计算机技术，1~2名

科研工作:

1.Tong C., Yan X. (2015): A novel decentralized process monitoring scheme using a modified multiblock PCA algorithm. Accepted for printing in:IEEE Transactions on Automation Science and Engineering.

2.Tong C.,Lan T.,Shi X. (2016): Double-layer ensemble monitoring of non-Gaussian processes using modified independent component analysis. Accepted for printing in: ISA Transactions.

3. Tong C.,Lan T.,Shi X. (2017): Fault detection and diagnosis of dynamic processes using weighted dynamic decentralized PCA approach.Chemometrics & Intelligent Laboratory Systems 161, 34-42.

4.Tong C.,Lan T.,Shi X. (2017): Ensemble modified independent component analysis for enhanced non-Gaussian process monitoring. Control Engineering Practice 58, 34-41.

5.Tong C.,Lan T.,Shi X. (2016): Statistical process monitoring based on orthogonal multi-manifold projections and a novel variable contribution analysis. ISA Transactions 65, 407-417.

6.Tong C.,Lan T.,Shi X.(2016): Soft sensing of non-Gaussian processes using ensemble modified independent component regression. Chemometrics & Intelligent Laboratory Systems 157, 120-126.

7.Tong C., Palazoglu A. (2016): Dissimilarity-based fault diagnosis through ensemble filtering of informative variables. Industrial & Engineering Chemistry Research 55(32), 8774-8783.

8.Tong C., Shi X. (2016): Decentralized monitoring of dynamic processes based on dynamic feature selection and informative fault pattern dissimilarity. IEEE Transactions on Industrial Electronics 63(6), 3804-3814.

9.Tong C., Palazoglu A., El-Farra N.H.,Yan X. (2015): Energy demand management for process systems through production scheduling and control. AIChE Journal 61(11), 3756-3769.

10.Tong C., El-Farra N.H., Palazoglu A. (2015): Energy demand response of process systems through production scheduling and control. IFAC-PapersOnline 48(8), 385-390.

11.Tong C., El-Farra N.H., Palazoglu A., Yan X.(2014): Fault detection and isolation in hybrid process systems using a combined data-driven and observer-design methodology. AIChE Journal 60(8), 2805-2814.

12.Tong C., Palazoglu A.,Yan X. (2014): Improved ICA for process monitoring based on ensemble learning and Bayesian inference. Chemometrics & Intelligent Laboratory Systems 135, 141-149.

13.Tong C., Yan X. (2014): Statistical process monitoring based on a multi-manifold projection algorithm. Chemometrics & Intelligent Laboratory Systems 130, 20-28.

14.Tong C., Song Y., Yan X. (2013): Distributed statistical process monitoring based on four-subspace construction and Bayesian inference. Industrial & Engineering Chemistry Research 52(29), 9897-9907.

15. Tong C., Palazoglu A., Yan X. (2013): An adaptive multimode process monitoring strategy based on mode clustering and mode unfolding. Journal of Process Control 23(10), 1497-1507.

(1)基于数据特征选择与匹配的工业过程监测方法研究国家自然科学基金编号：61503204

(2) 面向复杂特征数据的工业过程监测方法研究浙江省自然科学基金编号：Y16F030003