程軍聖

程軍聖，男，1968年10月生，博士，湖南大學機械與運載工程學院教授，博士生導師。

基本介紹

• 中文名：程軍聖
• 畢業院校：湖南大學
• 學位/學歷：博士
• 專業方向：機械與運載工程
• 任職院校：湖南大學

人物經歷

教育經歷

1987.9-1991.6 吉林工業大學工程機械系，學士；

1997.9-2000.6 湖南大學機械與汽車工程學院，碩士；

2002.9- 2005.6 湖南大學機械與汽車工程學院，博士。

工作經歷

1991.7-1997.8 湖南省湘南器材廠科研處，工程師；

2000.7-2002.5 湖南大學機械與汽車工程學院，工程師；

2002.6-2003.5 湖南大學機械與汽車工程學院，講師；

2003.6-2006.5 湖南大學機械與汽車工程學院，副教授；

2005.6-2008.9 湖南大學力學與航空航天學院，博士後；

2006.5- 湖南大學機械與運載工程學院，教授；

2007.7-湖南大學機械與運載工程學院，博士生導師

研究領域

專業領域：機械工程

主要研究方向：

1.模式識別與智慧型控制

研究模式識別與人工智慧及其在機械裝備智慧型監測與控制、機械工程領域大數據分析及信息挖掘、智慧型網聯汽車決策中的套用。

2.機器視覺與智慧型圖像處理

研究機器視覺與智慧型圖像處理技術及其在智慧型製造、智慧型網聯汽車環境感知中的套用。

3.智慧型運維與健康管理

研究複雜機械裝備故障機理、故障診斷與壽命預測、健康評價方法，研究複雜機械裝備智慧型運行及維護技術，開發複雜機械裝備智慧型運維與健康管理系統。

科研項目

主持的主要科研課題

[1] 深度凸包網路及其在大型旋轉機械壽命預測中的套用. 國家自然科學基金項目, 2019-2022

[2] 複雜機電系統服役質量監測檢測與維護質量控制. 國家重點研發計畫, 2016-2019

[3] 自適應最稀疏時頻分析方法及其在機械故障診斷中的套用. 國家自然科學基金項目, 2014-2017

[4] 內稟時間-特徵尺度分解方法及其在機械故障診斷中的套用研究. 國家自然科學基金項目, 2011-2013

[5] 局部均值分解方法及其在機械故障診斷中的套用研究. 國家自然科學基金項目, 2008-2010

[6] 大型風力發電機組狀態監控與故障診斷技術研究. 國家863項目, 2009-2011

[7] 內稟時間-尺度分解方法及其在機械故障診斷中的套用研究. 湖南省自然科學基金重點項目, 2011-2013

[8] 某型***振動評價與定量故障特徵提取. 軍工項目. 2019-2020

[9] 南京高速齒輪有限公司CMS系統開發. 橫向課題, 2015-2017

[10] 博世車用發電機噪聲控制. 橫向課題, 2009-2010

[11] 中石油海上設備振動測試與控制. 橫向課題, 2008-

[12] 乘用車儀表台振動試驗規範研究. 橫向課題, 2011.9-2012.9

[13] ***電池故障和壽命預測. 軍工項目, 2012-2015

[14] ***典型故障模擬與驗證技術研究. 軍工項目, 2015-2016

[15] 某型***電機振動測試與分析. 2012-2013

學術成果

發表的主要學術論文

[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): 04014077-1-10

[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