导师姓名：姜洪开
性别：
人气指数：166

所属院校：西北工业大学
所属院系：航空学院
职称：教授
导师类型：
招生专业：交通运输工程、航空宇航科学


通讯方式 :
电子邮件：jianghk@nwpu.edu.cn


个人简述 :
1、获工业和信息化部国防科技进步二等奖一项，飞行器XXXX故障识别技术，排名第一，20102、获陕西省第十一届自然科学优秀学术论文三等奖一项，排名第一，20103、被评为2010年度航空学院本科生和研究生教学最满意教师4、被评为2014年度航空学院全英文授课最满意老师5、被评为2015年度航空学院本科生教学最满意教师6、获得2013年西北工业大学教师讲课比赛高等专业技术职务组一等奖

科研工作 :
2002-2006，西安交通大学仪器科学与技术专业博士学位2006-2008，西北工业大学航空宇航科学与技术专业博士后2011-2012， 加拿大英属哥伦比亚大学机械工程系国家公派访问学者2008-2014，西北工业大学航空学院副教授2014-? ，西北工业大学航空学院教授、博士生导师 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，2015-20182、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，2010-20123、PHM软件验证设备，中航西安航空计算技术研究所项目，2017-20184、XXXX监测平台研制，西安空间无线电技术研究所项目，2017-20185、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，2016-20186、飞机XXXX故障诊断研究，航空科学基金项目，2013-20157、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，2013-20148、磁流变阻尼器半主动悬架控制系统设计，横向课题项目，2012－20139、民用飞机实时诊断与系统维护技术研究，千山电子项目，2008-201010、XXXX振动特性分析，航天一院项目，2008－200911、液压系统XXXX监测技术研究，航天科技创新基金项目，2006-200712、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 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. [2] 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.[3] 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.[4] 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: 1-14.2017[1] Shao Haidong, Jiang Hongkai*, Wang Fuan, Zhao Huiwei. An enhancement deep featurefusion method for rotating machinery fault diagnosis. Knowledge-Based Systems,2017, 119: 200-220. [2] Shao Haidong, Jiang Hongkai*, Zhao Huiwei, Wang Fuan. A novel deep autoencoderfeature learning method for rotating machinery fault diagnosis. MechanicalSystems and Signal Processing, 2017, 95: 187-204.[3] Jiang Hongkai*, Wang Fuan, Shao Haidong, Zhang Haizhou. Rolling bearingfault identification using multilayer deep learning convolutional neuralnetwork. Journal of Vibroengineering, 2017, 19(1): 1392-8716. [4] Shao Haidong, Jiang Hongkai*, Wang Fuan,Wang Yanan. Rolling bearing fault diagnosis using adaptive deep belief networkwith dual-tree complex wavelet packet. ISA Transactions, 2017, 69, 187-201.[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.[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 bearingfault feature extraction under variable conditions using hybrid order trackingand EEMD. Journal of Vibroengineering, 2016, 18(7): 2186-2242. [2] Shao Haidong, Jiang Hongkai*, Zhao Huiwei, Cai Qiushi. Aircraft electromechanicalsystem fault diagnosis based on deep learning. The 29th International Congresson Conditon Monitoring and Diagnostic Engineering Management, 2016.[3] WangYanan, Jiang Hongkai*, Zhao Huiwei,Meng Zhiyong. A deep model for aircraft engine fault diagnosis. The 29thInternational Congress on Conditon Monitoring and Diagnostic EngineeringManagement, 2016.[4] Wang Fuan, Jiang Hongkai*, Meng Zhiyong, Cai Qiushi, Zhang Haizhou. Rotatingmachinery fault diagnosis based on deep convolutional neural network. The 29thInternational Congress on Conditon Monitoring and Diagnostic EngineeringManagement, 2016.2015[1] Shao haidong, jiang hongkai*, zhang xun, niu maogui. Rolling bearing fault diagnosisusing an optimization deep belief network. Measurement Science and Technology,2015, 26: 115002(17pp). [2] Li limin, wangzhongsheng, jiang hongkai*. Storage battery remaining useful life prognosis usingimproved unscented particle filter. Proceedings of the institution ofmechanical engineerings Part O: journal of risk and reliability. 2015, 229(1): 52-61.2014[1] Jiang Hongkai*, Wang Han, Zhou Yong. An optimal lifting multiwaveletfor rotating machinery fault detection. Journal of Vibroengineering, 2014,16(1): 303-311. [2] Shao Haidong, Jiang Hongkai*. Research on semi-active suspensionvibration control using magneto-rheological damper. Proceedings of the FirstSymposium on Aviation Maintenance and Management, 2014, 2: 441-447.[3] Wang Han, Jiang Hongkai* , Guo Dong. Bearing Fault Diagnosis Basedon EEMD and AR Spectrum Analysis. Proceedings of the First Symposium onAviation Maintenance and Management, 2014, 1: 389-396. [4] Zhang Xueli, Jiang Hongkai*. Rolling bearing Fault Diagnosis Based on1.5-dimension spectrum. Proceedings of the First Symposium on AviationMaintenance and Management, 2014, 2: 433-440. [5] Niu Maogui, Jiang Hongkai*. Research on the Dynamic Model withMagnetorheological Damper. Proceedings of the First Symposium on AviationMaintenance and Management, 2014, 1: 323-330.[6] 李丽敏，王仲生，姜洪开*. 基于相似性传播聚类的航空发动机突发故障诊断.振动与冲击，2014，33(1):51-55.[7] 李丽敏，王仲生，姜洪开*. 基于多状态的MOG-HMMT和Viterbi的航空发动机突发故障预测，振动、测试与诊断，2014，34(2): 310-314.2013[1] Jiang Hongkai*, Li Chengliang, LiHuaxing. An improved EEMD with Multiwavelet Packet for Rotating MachineryMulti-fault Diagnosis. Mechanical Systems and Signal Processing, 2013, 36:225-239.　[2] Jiang Hongkai*, Xia Yong, WangXiaodong. Rolling bearing fault detection using an adaptive liftingmultiwavelet packet with a dimension spectrum. Measurement Science andTechnology, 2013, 24(12): 125002-125012.[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. [4] 姚培，王仲生，姜洪开*. 局部保形映射和Adaboost方法在滚动轴承故障诊断中的应用.振动与冲击，2013,32(5):155-159.[5] 姚培，王仲生，姜洪开*. 不均衡数据下基于CS-Boosting的故障诊断算法.振动、测试与诊断，2013,33(1):111-115.[6] 李城梁，王仲生，姜洪开*.自适应Hessian LLE在机械故障特征提取中的应用.振动工程学报，2013，26(5):758-763.[7] 李城梁，王仲生，姜洪开*.基于动态SSL的航空发动机突发故障检测.振动、测试与诊断，2013，33(3):461-465.2012[1] Jiang Hongkai*, Duan Chendong. AnAdaptive Lifting Scheme and The Application in Rolling Bearing Fault Diagnosis.Journal of Vibroengineering, 2012, 14(2): 759-770.[2] Jiang Hongkai*, He Yina, Yao Pei.Incipient Defect Identification in Rolling Bearings Using Adaptive LiftingScheme Packet. Journal of Vibroengineering, 2012, 14(2): 771-782. [3] Jiang Hongkai*, He Yina, DuanChendong. Rolling Bearing Fault Diagnosis Using Improved Lifting Scheme. AdvancedMaterials 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.[5] 姚培，王仲生，姜洪开*. 不均衡数据下CS-Boosting的故障诊断新算法.振动、测试与诊断，2012,33(1)：111－115.2011[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.[2] 芦玉华，王仲生，姜洪开*. 基于改进时变自回归模型的滚动轴承故障诊断.振动与冲击，2011,30(12)：74-77.[3] 陈晓理，王仲生，姜洪开*. 基于改进样板去噪源分离的轴承复合故障诊断.振动与冲击，2011,22(17)：2080-2084.2010[1] Wang Zhongsheng, Jiang Hongkai*. Robustincipient fault identification of aircraft engine rotor based on wavelet andfraction. Aerospace Science and Technology, 2010, 14(4): 221-224.2009[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.[3] Li Zhen, HeZhengjia, Zi Yanyang, Jiang Hongkai*.Rotating machinery fault diagnosis using signal-adapted lifting scheme.Mechanical Systems and Signal Processing, 2008, 22(3): 542-556.2007[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.[2] Duan Chengdong, He Zhengjia, Jiang Hongkai*. A slidingwindow feature extraction method for rotating machinery based on the liftingscheme. Journal of Sound and Vibration, 2007, 299(4-5): 774-785.2006[1] Jiang Hongkai*, He Zhengjia, Duan Chengdong, Chen Peng. Gearbox Fault? Diagnosis Using Adaptive RedundantLifting Scheme. Mechanical Systems and Signal Processing.2006, 20(8): 1992-2006.[2] 段晨东，姜洪开*，何正嘉.基于监测数据的特征小波构造及应用.长安大学学报,2006,26(2): 107-110.2005[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.[3] 段晨东，姜洪开*，何正嘉.非线性小波变换在故障特征提取中的应用.振动工程学报,2005,1: 129-132.2004[1]姜洪开*，何正嘉，段晨东.冗余第2代小波构造及机械信号特征提取.西安交通大学学报.2004,11：1140-1142.[2] 段晨东，姜洪开*，何正嘉.一种基于信号相关性检测的自适应小波变换及应用.长安大学学报,2004,7: 674-677.