【人物简介】

　　洪开，男，1972年生，博士，副教授，硕士生导师。。2006年4月获西安交通大学仪器科学与技术专业博士学位。2006年5月进入西北工业大学航空宇航科学与技术博士后流动站从事博士后研究工作，并留校任教。

【研究方向】

飞行器健康评估、预警与监控技术；结构微小损伤识别技术；网络化远程故障诊断；复合材料和结构件无损检测技术。

【研究成果】

　　已在国内外重要学术刊物上发表研究论文20余篇，其中SCI索引4篇，EI索引16篇，ISTP索引3篇。已申请中国职务发明专利2项。参与撰写研究生教材一部。

【科研项目】

　　主持科研项目： 1、基于深度学习的飞行器故障不确定性评估与预测研究，国家自然科学基金面上项目，2015-2018 2、基于提升多小波的航空发动机早期耦合故障诊断技术研究，国家自然科学基金面上项目，2010-2012 3、XXXX外挂振动环境预计方法研究，航空科学基金项目，2018-2020 4、PHM软件验证设备，中航西安航空计算技术研究所项目，2017-2018 5、XXXX监测平台研制，西安空间无线电技术研究所项目，2017-2018 6、民用飞机飞控系统状态监控及故障诊断技术研究，商飞客户服务有限公司基金项目，2016-2018 7、飞机XXXX故障诊断研究，航空科学基金项目，2013-2015 8、飞行器结构健康不确定性评估方法研究，陕西省自然科学基金面上项目，2013-2014 9、磁流变阻尼器半主动悬架控制系统设计，横向课题项目，2012－2013 10、民用飞机实时诊断与系统维护技术研究，千山电子项目，2008-2010 11、XXXX振动特性分析，航天一院项目，2008－2009 12、液压系统XXXX监测技术研究，航天科技创新基金项目，2006-2007 13、第二代小波构造与飞行器转子系统早期故障定量识别研究，中国博士后科学基金，2006-2008

【论文著作】

2018 [1] Shao Haidong, Jiang Hongkai*, Zhang Haizhou, Liang Tianchen. Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network. IEEE Transactions on Industrial Electronics, 2018, 65(3): 2727-2736. （JCR1区，IF=7.168） [2] Jiang Hongkai*, Li Xingqiu, Shao Haidong. Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network. Measurement Science and Technology, 2018, 29(6), 065107. （JCR2区，IF=1.585） [3] Shao Haidong, Jiang Hongkai*, Zhao Ke. A novel tracking deep wavelet auto-encoder method for intelligent fault diagnosis of electric locomotive bearings. Mechanical Systems and Signal Processing, 2018, 110: 193-209.（JCR1区，IF=4.116） [4] Shao Haidong, Jiang Hongkai*, Li Xingqiu. Rolling bearing fault detection using continuous deep belief netwok with locally linear embedding. Computers in Industry, 2018, 96: 27-39. （JCR2区，IF=2.691） [5] Shao Haidong, Jiang Hongkai*, Lin Ying, Li Xingqiu. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Mechanical Systems and Signal Processing, 2018, 102: 278-297. （JCR1区，IF=4.116） [6] Shao Haidong, Jiang Hongkai*, Zhang Haizhou, Duan Wenjing, Liang Tianchen, Wu Shuaipeng. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mechanical Systems and Signal Processing, 2018, 100: 743-765. （JCR1区，IF=4.116） [7] Shao Haidong, Jiang Hongkai*, Li Xingqiu, Wu Shuaipeng. Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowledge-Based Systems, 2018, 140: 1-14. （JCR1区，IF=4.529） 2017 [1] Shao Haidong, Jiang Hongkai*, Wang Fuan, Zhao Huiwei. An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowledge-Based Systems, 2017, 119: 200-220. （JCR1区，IF=4.529） [2] Shao Haidong, Jiang Hongkai*, Zhao Huiwei, Wang Fuan. A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing, 2017, 95: 187-204. （JCR1区，IF=4.116） [3] Jiang Hongkai*, Wang Fuan, Shao Haidong, Zhang Haizhou. Rolling bearing fault identification using multilayer deep learning convolutional neural network. Journal of Vibroengineering, 2017, 19(1): 1392-8716. （JCR4区，IF=0.398） [4] Shao Haidong, Jiang Hongkai*, Wang Fuan, Wang Yanan. Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet. ISA Transactions, 2017, 69, 187-201. （JCR1区，IF=3.394） [5] Wang Fuan, Jiang Hongkai*, Shao Haidong, Duan Wenjing, Wu Shuaipeng. An adaptive deep convolutional neural network for rolling bearing fault diagnosis. Measurement Science and Technology, 2017, 28(9): 095005. （JCR2区，IF=1.585） [6] Jiang Hongkai*, Shao Haidong, Chen Xinxia, Huang Jiayang. Aircraft fault diagnosis based on deep belief network. 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, 123-127. 2016 [1] Jiang Hongkai*, Cai Qiushi, Zhao Huiwei, Meng Zhiyong. Rolling bearing fault feature extraction under variable conditions using hybrid order tracking and EEMD. Journal of Vibroengineering, 2016, 18(7): 2186-2242. （JCR4区，IF=0.398） [2] Shao Haidong, Jiang Hongkai*, Zhao Huiwei, Cai Qiushi. Aircraft electromechanical system fault diagnosis based on deep learning. The 29th International Congress on Conditon Monitoring and Diagnostic Engineering Management, 2016. [3] Wang Yanan, Jiang Hongkai*, Zhao Huiwei, Meng Zhiyong. A deep model for aircraft engine fault diagnosis. The 29th International Congress on Conditon Monitoring and Diagnostic Engineering Management, 2016. [4] Wang Fuan, Jiang Hongkai*, Meng Zhiyong, Cai Qiushi, Zhang Haizhou. Rotating machinery fault diagnosis based on deep convolutional neural network. The 29th International Congress on Conditon Monitoring and Diagnostic Engineering Management, 2016. 2015 [1] Shao haidong, jiang hongkai*, zhang xun, niu maogui. Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science and Technology, 2015, 26: 115002(17pp). （JCR2区，IF=1.585） 2014 [1] Jiang Hongkai*, Wang Han, Zhou Yong. An optimal lifting multiwavelet for rotating machinery fault detection. Journal of Vibroengineering, 2014, 16(1): 303-311. （JCR4区，IF=0.398） [2] Shao Haidong, Jiang Hongkai*. Research on semi-active suspension vibration control using magneto-rheological damper. Proceedings of the First Symposium on Aviation Maintenance and Management, 2014, 2: 441-447. [3] Wang Han, Jiang Hongkai* , Guo Dong. Bearing Fault Diagnosis Based on EEMD and AR Spectrum Analysis. Proceedings of the First Symposium on Aviation Maintenance and Management, 2014, 1: 389-396. [4] Zhang Xueli, Jiang Hongkai*. Rolling bearing Fault Diagnosis Based on 1.5-dimension spectrum. Proceedings of the First Symposium on Aviation Maintenance and Management, 2014, 2: 433-440. [5] Niu Maogui, Jiang Hongkai*. Research on the Dynamic Model with Magnetorheological Damper. Proceedings of the First Symposium on Aviation Maintenance and Management, 2014, 1: 323-330. 2013 [1] Jiang Hongkai*, Li Chengliang, Li Huaxing. An improved EEMD with Multiwavelet Packet for Rotating Machinery Multi-fault Diagnosis. Mechanical Systems and Signal Processing, 2013, 36: 225-239.　（JCR1区，IF=4.116） [2] Jiang Hongkai*, Xia Yong, Wang Xiaodong. Rolling bearing fault detection using an adaptive lifting multiwavelet packet with a dimension spectrum. Measurement Science and Technology, 2013, 24(12): 125002-125012. （JCR2区，IF=1.585） [3] 姜洪开*，何毅娜.基于改进粒子滤波的飞机起落架损伤识别研究.西北工业大学学报, 2013,31（3）：397-400. 2012 [1] Jiang Hongkai*, Duan Chendong. An Adaptive Lifting Scheme and The Application in Rolling Bearing Fault Diagnosis. Journal of Vibroengineering, 2012, 14(2): 759-770. （JCR4区，IF=0.398） [2] Jiang Hongkai*, He Yina, Yao Pei. Incipient Defect Identification in Rolling Bearings Using Adaptive Lifting Scheme Packet. Journal of Vibroengineering, 2012, 14(2): 771-782. （JCR4区，IF=0.398） [3] Jiang Hongkai*, He Yina, Duan Chendong. Rolling Bearing Fault Diagnosis Using Improved Lifting Scheme. Advanced Materials Research, 2012, 518-523: 3780-3783. [4] 窦丹丹,姜洪开*.基于信息熵和SVM多分类的飞机液压系统故障诊断.西北工业大学学报, 2012,30(4): 529-534. 2011 [1] 姜洪开*，窦丹丹.基于自适应第二代小波的超声回波信号特征识别.西北工业大学学报, 2011,29（1）：93-96. 2010 [1] Wang Zhongsheng, Jiang Hongkai*. Robust incipient fault identification of aircraft engine rotor based on wavelet and fraction. Aerospace Science and Technology, 2010, 14(4): 221-224. （JCR4区，IF=2.057） 2009 [1] 王仲生，姜洪开*，徐一艳.发动机转子系统早期故障智能诊断.航空学报, 2009,30(2)：242-246. 2008 [1] 姜洪开*，王仲生. 基于改进第二代小波算法的发电机碰摩故障特征提取.中国电机工程学报,2008,28(8): 127-131. [2] 姜洪开*，王仲生. 基于自适应提升小波包的故障微弱信号特征早期识别.西北工业大学学报,2008,26(1): 99-103. 2007 [1] 姜洪开*，王仲生.第二代小波包构造及航空发动机损伤识别.北京航空航天大学学报.2007,33(7)：777-780. 2006 [1] Jiang Hongkai*, He Zhengjia, Duan Chengdong, Chen Peng. Gearbox Fault Diagnosis Using Adaptive Redundant Lifting Scheme. Mechanical Systems and Signal Processing.2006, 20(8): 1992-2006. （JCR1区，IF=4.116） 2005 [1] 姜洪开*，何正嘉，段晨东，陈雪峰.自适应冗余第2代小波设计及齿轮箱故障特征提取.西安交通大学学报.2005,7：715-718. [2] 姜洪开*，何正嘉，段晨东，陈雪峰.基于提升方法的小波构造及早期故障特征提取.西安交通大学学报.2005,5：494-498.