Title：Associated Professor

 E-mail：zhanghanwen@ustb.edu.com

Office：1026

 Education：

2014.9-2018.6 Ph.D , Tsinghua University, control science and engineering

 Work Experience：

2018.10-2021.1  Postdoctor, the State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University

2021.10-present  Associated Professor, School of Automation and Electrical Engineering, University of Science and Technology Beijing

Research Interests：

Process monitoring, prognostics and health management, and digital twin.

Courses：Programmable Logic Controller

Selected Publications：

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

[4] 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. 

[5] 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.

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

[7] 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.

[8] 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.

[9] 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.

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

[11] 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.

[12] 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. 

[13] 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. 

[14] 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. 

[15] 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. 

Selected Projects：

[1]  National Natural Science Foundation of China, Research on modeling and remaining life prediction for non-stationary and dependent incremental degradation processes of industrial equipment

[2]  China Postdoctoral Science Foundation: Industrial equipment degradation modeling and remaining life prediction based on multi-source monitoring data

[3]  China Postdoctoral Science Foundation: Research on non-stationary process monitoring for blast furnaces

[4]  Zhejiang Postdoctoral Foundation: Non-markovian degradation modeling and remaining life prediction of industrial equipment under multiple operating conditions

[5]  Sub-project of Industrial Internet Innovation and Development Project: Research on the diagnosis method for abnormal furnace conditions of blast furnaces and development of Industrial APP

[6]  Open Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University