[1] Zhang Hanwen, Chen Maoyin, Xi Xiaopeng, Zhou Donghua. Remaining useful life prediction for degradation processes with long-range dependence. IEEE Transactions on Reliability, 2017, 66(4): 1368-1379.

[2] Zhang Hanwen, Zhou Donghua, Chen Maoyin, Shang Jun. FBM-based remaining useful life prediction for degradation processes with long-range dependence and multiple modes. IEEE Transactions on Reliability, 2019, 68(3): 1021-1033. 

[3] Zhang Hanwen, Shang Jun, Zhang Jianxun, Yang Chunjie. Nonstationary process1monitoring for blast furnaces based on consistent trend feature analysis. IEEETransactions on Control Systems Technology, 2022, 30(3): 1257 - 1267.

[4] Zhang Hanwen, Jia chao, Chen Maoyin. Remaining Useful Life Prediction for Degradation Processes with Dependent and Non-Stationary Increments. IEEE Transactions on Instrumentation and Measurement, 2021.

[5] Zhang Hanwen, Zhou Donghua, Chen Maoyin, Xi Xiaopeng. Predicting remaining useful life based on a generalized degradation with fractional Brownian motion. Mechanical Systems and Signal Processing, 2019, 115: 736-752. 

[6] Zhang Hanwen, Chen Maoyin, Shang Jun, Yang Chunjie, Sun Youxian. Stochasticprocess-based degradation modeling and RUL prediction: from Brownian motionto fractional Brownian motion. Science China Information Sciences, 2021, 64:171201.

[7] Zhang Hanwen, Shang Jun, Yang Chunjie, Sun Youxian. Conditional random field for monitoring multimode processes with stochastic perturbations. Journal of the Franklin Institute, 2020, 357(12): 8229-8251.

[8] Zhang Hanwen, Chen Maoyin, Zhou Donghua. Remaining useful life prediction for a nonlinear multi-degradation system with public noise. Journal of Systems Engineering and Electronics, 2018, 29(2): 429-435.

[9] Zhang Hanwen, Chen Maoyin, Zhou Donghua. Predicting remaining useful life for a multi-component system with public noise. Prognostics and System Health Management Conference , 2016. IEEE, 2016: 1-6.

[10] Zhang Hanwen, Chen Maoyin, Zhou Donghua. Remaining useful life prediction for nonlinear degrading systems with maintenance. Prognostics and System Health Management Conference , 2017. IEEE, 2017: 1-6.

[11] Zhang Hanwen, Yang Chunjie, Sun Youxian. Remaining useful life prediction under multiple fault patterns for degradation processes with long-range dependence,  2019 CAA SAFEPROCESS.

[12] Shang Jun, Chen Maoyin, Zhang Hanwen. Fault detection based on augmented kernel Mahalanobis distance for nonlinear dynamic processes, Computers & Chemical Engineering, 2018, 109: 311-321.

[13] Xi Xiaopeng, Chen Maoyin, Zhang Hanwen, Zhou Donghua. An improved non-Markovian degradation model with long-term dependency and item-to-item uncertainty. Mechanical Systems and Signal Processing, 2018, 105: 467-480. 

[14] Shang Jun, Chen Maoyin, Zhang Hanwen, Ji Hongquan, Zhou Donghua, Zhang Haifeng, Li Mingliang. Increment-based recursive transformed component statistical analysis for monitoring blast furnace iron-making processes: An index-switching scheme. Control Engineering Practice, 2018, 77: 190-200. 

[15] Xi Xiaopeng, Zhou Donghua, Chen Maoyin, Narayanaswamy Balakrishnan, Zhang Hanwen. Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes. Quality and Reliability Engineering International, 2020, 36(4): 1402-1421. 

[16] Shang Jun, Zhou Donghua, Chen Maoyin, Ji Hongquan, Zhang Hanwen. Incipient sensor fault diagnosis in multimode processes using conditionally independent Bayesian learning based recursive transformed component statistical analysis. Journal of Process Control, 2019, 77: 7-19. 

[17] 李彦瑞, 杨春节, 张瀚文, 李俊方.流程工业数字孪生关键技术探讨.自动化学报,2021,47(03):501-514.

[18] 朱雄卓, 张瀚文, 杨春节.基于高斯混合模型的MWPCA高炉异常监测算法.化工学报, 2021, 72(03):1539-1548.