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代表性论著
专著：
Performance Assessment for Process Monitoring and Fault Detection Methods，Springer，ISBN: 978-3-658-15970-2，2016.
代表性论文：
[1] Kai Zhang, Haiyang Hao, Zhiwen Chen, Steven X. Ding, and Kaixiang Peng, A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches, Journal of Process Control, vol. 33, pp. 112-126, 2015. （SCI）
[2] Kai Zhang, Yuri A. W. Shardt, Zhiwen Chen, and Kaixiang Peng, Using the expected detection delay to assess the performance of different multivariate statistical process monitoring methods for multiplicative and drift faults, ISA Transactions, vol. 67, no. 6, pp. 56-66, 2017. （SCI）
[3] Kai Zhang, Haiyang Hao, Zhiwen Chen, Steven X. Ding, Kaixiang Peng, and Eve L. Ding, Comparison study of multivariate statistics based key performance indicator monitoring approaches, 19th IFAC world congress, Cape Town, South Africa, 2014, pp.10628-10633. （EI）
[4] Kai Zhang, Yuri A. W. Shardt, Zhiwen Chen, Steven X. Ding, and Kaixiang Peng, Unit-level modelling for KPI of batch hot strip mill process using dynamic partial least squares, 17th IFAC SYSID, Beijing, China, 2015, pp. 1005-1010. （EI）
[5] Kai Zhang, Jie Dong, and Kaixiang Peng, A novel dynamic non-Gaussian approach for quality-related fault diagnosis with application to the hot strip mill process, Journal of the Franklin Institute, vol. 354, no. 2, pp. 702-721, 2017. （SCI）
[6] Kai Zhang, Steven X. Ding, Yuri A. W. Shardt, Zhiwen Chen, and Kaixiang Peng, Assessment of T2- and Q-statistics for detecting additive and multiplicative faults, Journal of the Franklin Institute, vol. 354, no. 2, pp. 668-688, 2017. （SCI）
[7] Kai Zhang, Yuri A. W. Shardt, Steven X. Ding, Zhiwen Chen and Kaixiang Peng, A brief survey of test statistics for detecting multiplicative faults, IEEE CDC 2016, Las vegas, USA, pp. 2152-2157. （EI）
[8] Kai Zhang, Yuri A. W. Shardt, Zhiwen Chen, Steven X. Ding, and Kaixiang Peng, A KPI-based process monitoring framework for large-scale processes, ISA Transactions, vol. 68, 276-286, 2017. （SCI）
[9] Zhiwen Chen, Kai Zhang, Yuri A.W. Shardt, and Steven X. Ding, Comparison of two basic statistics for fault detection and process monitoring, IFAC world congress 2017, Toulouse, France. （EI）
[10] Kaixiang Peng, Kai Zhang*, Gang Li, and Donghua Zhou, Contribution rate plot for nonlinear quality-related fault diagnosis with application to the hot strip mill process, Control Engineering Practice, vol. 24, no. 4, pp. 360-369, 2013. （SCI）
[11] Kaixiang Peng, Kai Zhang*, and Gang Li, Quality-related process monitoring based on total kernel PLS model and its industrial application, Mathematical Problems in Engineering, vol. 2013, pp. 1-14, 2013. （SCI）
[12] Kaixiang Peng, Kai Zhang*, Xiao He, Gang Li, and Xu Yang, New kernel independent and principal components analysis-based process monitoring approach with application to hot strip mill process, IET Control Theory & Applications, vol. 8. no. 16, pp. 1723-1731, 2014. （SCI）
[13] Kaixiang Peng, Kai Zhang*, Jie Dong, and Xu Yang, A new data-driven process monitoring scheme for key performance indictors with application to hot strip mill process, Journal of the Franklin Institute, vol. 351, no. 9, pp. 4555-4569, 2014. （SCI）
[14] Kaixiang Peng, Kai Zhang*, and Gang Li, Online Contribution rate based fault diagnosis for nonlinear industrial processes, Acta Automatica Sinica, vol. 40, no. 3, pp. 423-430, 2014. （SCI）
[15] Kaixiang Peng, Kai Zhang*, Jie Dong, and Bo You, Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and multi-batch measurements, Journal of the Franklin Institute, vol. 352, no. 3, pp. 987-1006, 2014. （SCI）
[16] Jie Dong, Kai Zhang*, Ya Huang, Gang Li, and Kaixiang Peng, Adaptive total PLS based quality-relevant process monitoring with application to the Tennessee Eastman process, Neurocomputing, vol. 154, pp. 77-85, 2015. （SCI）
[17] Kaixiang Peng, Kai Zhang*, Bo You, and Jie Dong, Quality-relevant fault detection based on efficient projection to latent structures with application to hot strip mill process, IET Control Theory & Applications, vol. 9, no. 7, pp. 1135-1145, 2015. （SCI）
[18] Kaixiang Peng, Kai Zhang*, Bo You, and Jie Dong, Quality-related prediction and monitoring of multi-mode processes using multiple PLS with application to an industrial hot strip mill, Neurocomputing, vol. 168, pp. 1094-1103, 2015. （SCI）
[19] Kaixiang Peng, Kai Zhang*, Bo You, Jie Dong, and Zidong Wang, A Quality-based nonlinear fault diagnosis framework focusing on industrial multimode batch processes, IEEE Transactions on Industrial Electronics, vol. 63, no.4, pp. 2615-2624, 2016. （SCI）
[20] Zhiwen Chen, Steven X. Ding, Kai Zhang, and Zhikun Hu, Canonical correlation analysis-based fault detection methods with application to alumina evaporation process, Control Engineering Practice, vol. 46, pp. 51-58, 2016. （SCI）
[21]Liang Ma, Jie Dong, Kaixiang Peng, and Kai Zhang, A novel data-based quality-related fault diagnosis scheme for fault detection and root cause diagnosis with application to hot strip mill process, Control Engineering Practice, vol. 47, pp. 43-51, 2017. （SCI）
[22] YAW Shardt , S Mehrkanoon , Kai Zhang , X Yang, J Suykens, Modelling the Strip Thickness in Hot Steel Rolling Mills Using Least‐Squares Support Vector Machines, Canadian Journal of Chemical Engineering , accepted. （SCI）
[23] Haiyang Hao, Kai Zhang, Steven X. Ding, Zhiwen Chen, and Yaguo Lei, A data-driven multiplicative fault diagnosis approach for automation processes, ISA Transactions, vol. 53, no. 5, pp. 1436-1445, 2014. （SCI）
[24] Zhiwen Chen, Kai Zhang, Steven X. Ding, Yuri, and A. W. Shardt, Improved canonical correlation analysis-based fault detection methods for industrial processes, Journal of Process Control, vol. 41, 26-34, 2016. （SCI）
[25]Zhiwen Chen, Steven X. Ding, Hao Luo, Kai Zhang , An alternative data-driven fault detection scheme for dynamic processes with deterministic disturbances, accepted by Journal of the Franklin Institute, vol. 354, no.1 , 556–570, 2017.（SCI）
[26] Kaixiang Peng, Qianqian Li, Kai Zhang, Jie Dong, Quality-related process monitoring for dynamic non-Gaussian batch process with multi-phase using a new data-driven method, Neurocomputing, ,vol. 214, 317-328, 2016. (SCI)
[27]彭开香，马亮，张凯，复杂工业过程质量相关的故障检测与诊断技术综述，自动化学报，vol.43, no.3, pp.433-353, 2017. (EI)
[28]彭开香，李钢，张凯，基于动态全潜结构投影的热连轧厚度监控，控制理论与应用，vol.29, no.11, pp.1446-1451, 2012. (EI)
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