机械制造及其自动化专业博士，力学博士后。“Current Development in Theory and Applications of Wavelets”、“Journal of Measurement Science and Instrumentation”、“振动与冲击”、“振动、测试与诊断”等期刊编委，湖南省故障诊断与失效分析学会副理事长，中国故障诊断专业委员会常务理事。近年来一直从事模式识别与智能控制、机器视觉与智能图像处理、智能运维与健康管理等方面的教学和科研工作，在该领域主持并参与了国家重点研发计划、国家自然科学基金项目、国家863项目等40余项科研课题。在国内外学术期刊上发表相关论文300余篇，其中SCI、EI收录200余篇，编写专著5本。




基本信息

姓 名：程军圣
职 称：教授、博士生导师
学 位：博士/力学博士后
工作单位：湖南大学机械与运载工程学院
通讯地址：湖南长沙，湖南大学机械与运载工程学院
邮政编码：410082
Email：chengjunsheng@hnu.edu.cn
电 话：
教育背景
学历：1987.9-1991.6 吉林工业大学工程机械系，学士；
1997.9-2000.6 湖南大学机械与汽车工程学院，硕士；
2002.9- 2005.6 湖南大学机械与汽车工程学院，博士。
工作履历
研究工作简历：
1991.7-1997.8 湖南省湘南器材厂科研处，工程师；
2000.7-2002.5 湖南大学机械与汽车工程学院，工程师；
2002.6-2003.5 湖南大学机械与汽车工程学院，讲师；
2003.6-2006.5 湖南大学机械与汽车工程学院，副教授；
2005.6-2008.9 湖南大学力学与航空航天学院，博士后；
2006.5- 湖南大学机械与运载工程学院，教授；
2007.7- 湖南大学机械与运载工程学院，博士生导师

研究领域
专业领域：机械工程
主要研究方向：
1.模式识别与智能控制
研究模式识别与人工智能及其在机械装备智能监测与控制、机械工程领域大数据分析及信息挖掘、智能网联汽车决策中的应用。
2.机器视觉与智能图像处理
研究机器视觉与智能图像处理技术及其在智能制造、智能网联汽车环境感知中的应用。
3.智能运维与健康管理
研究复杂机械装备故障机理、故障诊断与寿命预测、健康评价方法，研究复杂机械装备智能运行及维护技术，开发复杂机械装备智能运维与健康管理系统。

科研项目
主持的主要科研课题
[1] 深度凸包网络及其在大型旋转机械寿命预测中的应用. 国家自然科学基金项目, 2019-2022
[2] 复杂机电系统服役质量监测检测与维护质量控制. 国家重点研发计划, 2016-2019
[3] 自适应最稀疏时频分析方法及其在机械故障诊断中的应用. 国家自然科学基金项目, 2014-2017
[4] 内禀时间-特征尺度分解方法及其在机械故障诊断中的应用研究. 国家自然科学基金项目, 2011-2013
[5] 局部均值分解方法及其在机械故障诊断中的应用研究. 国家自然科学基金项目, 2008-2010
[6] 大型风力发电机组状态监控与故障诊断技术研究. 国家863项目, 2009-2011
[7] 内禀时间-尺度分解方法及其在机械故障诊断中的应用研究. 湖南省自然科学基金重点项目, 2011-2013
[8] 某型***振动评价与定量故障特征提取. 军工项目. 2019-2020
[9] 南京高速齿轮有限公司CMS系统开发. 横向课题, 2015-2017
[10] 博世车用发电机噪声控制. 横向课题, 2009-2010
[11] 中石油海上设备振动测试与控制. 横向课题, 2008-
[12] 乘用车仪表台振动试验规范研究. 横向课题, 2011.9-2012.9
[13] ***电池故障和寿命预测. 军工项目, 2012-2015
[14] ***典型故障模拟与验证技术研究. 军工项目, 2015-2016
[15] 某型***电机振动测试与分析. 2012-2013


学术成果
发表的主要学术论文
[1] An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis.Neurocomputing, 2019, 359:77-92
[2] Rolling bearing fault diagnosis and performance degradation assessment under variable operation conditions based on nuisance attribute projection. Mechanical Systems and Signal Processing, 2019, 114: 165-188
[3] Linear maximum margin tensor classification based on flexible convex hulls for fault diagnosis of rolling bearings. Knowledge-Based Systems,2019, 173: 62-73
[4] Rolling bearing performance degradation assessment based on convolutional sparse combination learning. IEEE Access, 2019, 7: 17834-17846
[5] A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy. Mechanism and Machine Theory, 2013, 70: 441-453
[6] Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis. Mechanical Systems and Signal Processing，2013, 40(1): 136-153
[7] Partly ensemble empirical mode decomposition: An improved noise-assisted method for eliminating mode mixing. Signal Processing, 2014, 96(1): 362-374
[8] Adaptive sparsest narrow-band decomposition method and its applications to rolling element bearing fault diagnosis. Mechanical Systems and Signal Processing,2017, 85: 947-962
[9] Adaptive sparsest narrow-band decomposition method and its applications to rotor fault diagnosis. Measurement, 2016, 91: 451-459
[10] An intelligent fault diagnosis model for rotating machinery based on multi-scale higher order singular spectrum analysis and GA-VPMCD. Measurement，2016, 87: 38-50
[11] An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings. Frontiers of Mechanical Engineering, 2017, 1:1-10
[12] Roller bearing fault diagnosis method based on chemical reaction optimization and support vector machine. Journal of Computing in Civil Engineering, 2015, 29(5): **-1-10
[13] Gears fault diagnosis method using ensemble empirical mode decomposition energy entropy assisted ACROA-RBF neural network. Journal of Computational and Theoretical Nanoscience, 2016, 13: 1-11
[14] An integrated generalized discriminant analysis method and chemical reaction support vector machine model (GDA-CRSVM) for bearing fault diagnosis. Advances in Production Engineering & Management, 2017, 12(4): 321-336
[15] A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination. Mechanism and Machine Theory, 2014, 78: 187-200
[16] Multi-scale permutation entropy and its application to rolling bearing fault diagnosis. Shock and Vibration, 2014, Article ID 154291, 8 pages, doi:10.1155/2014/154291
[17] A roller bearing fault diagnosis method based on LCD energy entropy and ACROA-SVM. Shock and Vibration, 2014, Article ID 825825, 8 pages, doi:10.1155/2014/825825
[18] Application of frequency separation method based up EMD and local Hilbert energy spectrum method to gear fault diagnosis. Mechanism and Machine Theory, 2008, 43: 712-723
[19] Local rub-impact fault diagnosis of the rotor systems based on EMD. Mechanism and Machine Theory, 2009, 44: 784-791
[20] Application of SVM and SVD technique based on EMD to the fault diagnosis of the rotating machinery. Shock and Vibration, 2009, 16: 89-98
[21] A Fault diagnosis approach for gears based on IMF AR model and SVM. EURASIP Journal on Advances in Signal Processing. Volume 2008, Article ID 647135, 7 pages
[22] Time-energy density analysis based on wavelet transform. NDT&E International, 2005, 38(7): 569-572
[23] The application of energy operator demodulation approach based on EMD in machinery fault diagnosis. Mechanical Systems and Signal Processing , 2007, 21(2): 668-677
[24] Research on the intrinsic mode function (IMF) criterion in EMD method. Mechanical Systems and Signal Processing, 2006, 20(4): 817-824
[25] Application of support vector regression machines to the processing of end effects of Hilbert-Huang transform. Mechanical Systems and Signal Processing, 2007, 21(3): 1197-1211
[26] Application of an impulse response wavelet to fault diagnosis of rolling bearings. Mechanical Systems and Signal Processing, 2007, 21(2): 920-929
[27] A fault diagnosis approach for roller bearings based on EMD method and AR model. Mechanical Systems and Signal Processing, 2006, 20(2): 350-362
[28] Application of the improved generalized demodulation time-frequency analysis method to multi-component signal decomposition. Signal Processing, 2009, 89(6): 1205-1215
[29] The envelope order spectrum based on generalized demodulation time-frequency analysis and its application to gear fault diagnosis. Mechanical Systems and Signal Processing, 2010, 24(1): 508-521
[30] An order tracking technique for the gear fault diagnosis using local mean decomposition method. Mechanism and Machine Theory, 2012, 55: 67-76