专利成果
[1] 彭敏俊,王航等. 一种基于在线仿真的核电站系统级状态监测方法,国家发明专利,公开号:105955069A.
[2] 彭敏俊,王航等. 一种混合式核电站故障诊断方法,国家发明专利,公开号:201710608145.3.
[3] 王航,彭敏俊,夏庚磊等. 一种核动力设备的非平稳信号状态监测方法及系统[P]. 中国,专利申请号2020107768402, 2020.08.
[4] 王航,彭敏俊,王晓昆等. 一种基于变分模态分解的动力设备异常检测方法及系统[P]. 中国,专利申请号2020109081207, 2020.09.
[5] 王航,虞越,彭敏俊等. 一种核动力装置运行参数异常检测方法及系统 [P]. 中国,专利申请号2020106547154, 2020.07.
[6] 王航, 彭敏俊, 邓强等. 一种核动力装置故障的诊断方法及系统[P]. 中国,专利申请号2020109087449, 2020.09.
[7] 王航, 彭敏俊, 夏庚磊等.一种核动力装置故障诊断方法及系统[P]. 中国, 专利申请号2020109083838, 2020.09.
[8] 王航, 彭敏俊, 夏庚磊等. 一种基于时间卷积网络的电动闸阀剩余使用寿命预测方法[P]. 中国, 专利申请号202010655443X, 2020.07.
[9]王航, 徐仁义, 彭敏俊等. 基于正则化粒子滤波的电动闸阀故障确定方法及系统[P]. 中国, 专利申请号2020107769890, 2020.08.
[10] 王元,王航等. 一种基于SDG和神经网络的船用核动力装置故障诊断方法,国防发明专利,授权号:CN103761567A.
[11] 彭敏俊,王航等. 核动力装置分布式状态监测系统[简称:NPPDOMS] V1.0,计算机软件著作权,登记号:2017SR423810.
[12] 彭敏俊,王航等. 核动力装置引导规程系统[简称:NPPCOPS] V1.0,计算机软件著作权,登记号:2017SR454102.
发表论文
[1] H. Wang*, M.J. Peng, Z. Miao, et al. Remaining useful life prediction techniques for electric valves based on convolution auto encoder and long short term memory[J]. ISA Transactions, 2021(108): 333-342.
[2] H. Wang*, M.J. Peng, A. Ayodeji, et al. Advanced fault diagnosis method for nuclear power plant based on convolutional gated recurrent network and enhanced particle swarm optimization[J]. Annals of Nuclear Energy, 2021, 151: 107934.
[3] H. Wang*, M.J. Peng, R.Y. Xu, et al. Remaining Useful Life Prediction Based on Improved Temporal Convolutional Network for Nuclear Gate Valves, Frontiers in Nuclear Energy, 2020, 11: 584463.
[4] H. Wang*, M.J. Peng, Y.K. Liu, et al. Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM, Science and Technology of Nuclear Installations, 2020: 8349349.
[5] Hang WANG (*), Min-jun Peng, Yue Yu, et al. Fault identification and diagnosis based on KPCA and similarity clustering for nuclear power plants. Annals of Nuclear Energy, 2020, 85: 259-270.
[6] Y. Yu, M.J. Peng, H. Wang*, et al. Improved PCA model for multiple fault detection, isolation and reconstruction of sensors in nuclear power plant[J]. Annals of Nuclear Energy, 2020, 148: 107662.
[7] Hanan A. Saeed, Hang WANG (*), Min-jun Peng, et al. Online fault monitoring based on deep neural network and sliding window technique. Progress in Nuclear Energy, 2020 (121): 103236
[8] Hanan A. Saeed, Min-jun Peng, Hang WANG (*), Bo-wen Zhang. Novel fault diagnosis scheme utilizing deep learning networks. Progress in Nuclear Energy, 2020 (118): 103066
[9] Hang WANG (*),Min-jun PENG, et al. A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants. ISA Transactions, 2019, 95: 358-371.
[10] Min-jun PENG(*), Hang WANG, Shan-shan CHEN, et al. An Intelligent Hybrid Methodology of On-line System-level Fault Diagnosis for Nuclear Power Plant. Nuclear Engineering and Technology, 2018, 50(3): 396-410.
[11] Min-jun PENG(*), Hang WANG, Xu YANG, et al. Real-time Simulations to Enhance Distributed On-line Monitoring and Fault Detection in Pressurized Water Reactors. Annals of Nuclear Energy, 2017, 85: 259-270.
[12] Hang WANG, Min-jun PENG (*), et al. On-line Monitoring for Turbine Bypass System Based on real-time simulation. 10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017 Volume 1, 2017, Pages 47-56, San Francisco, United States.
[13] Hang WANG (*),Min-jun PENG, et al. An integrated data-driven methodology for early fault detection and diagnosis in nuclear power plant. International Journal of Nuclear Safety and Simulation (IJNS), 2017, Vol.8 No.3: 225-238.
[14] Hang WANG(*), Min-jun PENG, Peng WU, Shou-yu CHENG. Improved methods of online monitoring and prediction in condensate and feed water system of nuclear power plant. Annals of Nuclear Energy, 2016, 90: 44-53.
[15] Hang WANG (*),Min-jun PENG and Shou-yu CHENG. Online Fault diagnosis and Prediction of Condenser in Nuclear Power Plant. ANS 2015 Winter Meeting & Expo, Washington D.C, America, November 7-13, 2015.
[16] 王航*,彭敏俊、徐仁义、刘永阔. 基于卷积核和GRU网络的核电厂阀门故障预测方法[J]. 核电(重大设备可靠性提升及数据智能应用专辑), 2020, 5: 49-54.
[17] Liu, Jie; Peng, Minjun; Wang, Hang; Gong, Meijie, Development of distributed performance monitoring and analysis system for nuclear power Plant., 27th International Conference on Nuclear Engineering: Nuclear Power Saves the World!, ICONE 2019 , 2019-05-19 To 2019-05-24.
[18] 郭良壮, 彭敏俊, 王航, 等. 核电厂蒸汽旁排系统故障诊断与状态预测方法研究[C]. 中国核学会2017年学术年会. 威海市. 2017:321-326.
[19] ZHU Hai-shan,WANG Hang, et al. Development and analysis of a detailed parametric simulation model of condensers for nuclear power plants[J]. International Journal of Nuclear Safety and Simulation (IJNS). 2014,5(4):347-357.
[20]Li Wei, Peng Min-jun, Wang Hang, et al. Design of comprehensive diagnosis system in nuclear power plant[J]. Annals of Nuclear Energy. 2017,109:92-102.