[1]傅鹏程,王昌明,段俊斌,等.基于故障演化分析的齿轮故障预测特征选择[J].深圳大学学报理工版,2015,32(4):434-440.[doi:10.3724/SP.J.1249.2015.04434]
 Fu Pengcheng,Wang Changming,et al.A feature selection for fault prognosis based on fault evolution analysis[J].Journal of Shenzhen University Science and Engineering,2015,32(4):434-440.[doi:10.3724/SP.J.1249.2015.04434]
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基于故障演化分析的齿轮故障预测特征选择()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第32卷
期数:
2015年第4期
页码:
434-440
栏目:
电子与信息科学
出版日期:
2015-07-16

文章信息/Info

Title:
A feature selection for fault prognosis based on fault evolution analysis
文章编号:
201504016
作者:
傅鹏程12王昌明1段俊斌2谭晓栋3
1) 南京理工大学机械工程学院,南京 210094
2)北京特种机电技术研究所,北京 100012
3)武警警官学院电子技术系,成都 610213
Author(s):
Fu Pengcheng1 2 Wang Changming1 Duan Junbin2 and Tan Xiaodong3
1) School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, P.R.China
2) Beijing Special Electromechanical Technology Research Institute, Beijing 100012, P.R.China
3) Department of Electronic Technology, Officers College of CAPF, Chengdu 610213, P.R.China
关键词:
机械动力学与振动齿轮特征选择故障预测故障演化分析跟踪能力小波熵
Keywords:
mechanical dynamics and vibration gear feature selection fault prognosis fault evolution analysis track ability wavelet entropy
分类号:
TP 165
DOI:
10.3724/SP.J.1249.2015.04434
文献标志码:
A
摘要:
提出基于故障演化分析的齿轮故障预测特征选择方法.根据机械动力学与振动模型,计算不同故障严重程度下系统的输出响应.使用特征选择方法建立不同特征描述的齿轮故障演化曲线,计算各特征对齿轮故障演化过程的跟踪能力,选择跟踪能力最大的特征作为故障预测特征.以一阶齿轮传动系统的齿轮裂纹故障为例,论证结果表明,小波熵对齿轮裂纹故障演化过程的跟踪能力最大,采用小波熵作为齿轮裂纹故障的预测特征能有效提高故障预测的精度.
Abstract:
In order to improve the reliability and the effectiveness of gear fault prognosis, we propose a feature selection based on fault evolution analysis. Firstly, we build a mechanical dynamics and vibration model, which accordingly generates dynamic responses with tooth damage growth. Secondly, we obtain the trend curves of gear fault evolution for several features, and calculate the track ability for gear fault evolution of each feature. The feature with the strongest track ability is chosen as the feature for gear fault prognosis. Finally, we use a gear crack fault in a one-stage gearbox to verify the performance of the proposed method. Results show that the wavelet energy entropy with the optimal track ability for gear crack evolution among several selected features is the most suitable feature for gear crack fault prognosis.

参考文献/References:

[1] Dong Ming,He D.A segmental hidden semi-Markov model based diagnosis framework and methodology[J].Mechanical Systems and Signal Processing,2007,21(5):2248-2266.
[2] Medjaher K,Camci F, Zerhouni N .Feature extraction and evaluation for health assessment and failure prognostics[C]// Proceedings of First European Conference of the Prognostics and Health Management Society.Dresden(Germany):[s. n.],2012:111-116.
[3] Bin G F, Gao J J,Li X J, et al.Early fault diagnosis of rotating machinery based on wavelet packets:empirical mode decomposition feature extraction and neural network[J].Mechanical Systems and Signal Processing,2012,27:696-711.
[4] Miao Qiang,Makis V.Extraction of machinery health index in CBM based on wavelet modulus maxima[C]// Flexible Automation and Intelligent Manufacturing.Toronto:[s.n.],2004:959-965.
[5] Vecer P,Kreidl M,Smíd R.Condition indicators for gearbox condition monitoring systems[J].Acta Polytechnical,2005,45(6):35-43.
[6] Luo Jianlu,Tan Xiaodong,Liu Ying,et al. A feature selection for fault prognosis based on fault evolution analysis: China, 2014105317751[P].2014-10-10.(in Chinese)
罗建禄,谭晓栋,刘颖,等.一种基于故障演化分析的故障预测特征选择方法:中国,2014105317751[P].2014-10-10.
[7] Lyu Kehong,Tan Xiaodong,Liu Guanjun,et al.Sensor selection of helicopter transmission systems based on physical model and sensitivity analysis[J].Chinese Journal of Aeronautics,2014,27(3):643-654.
[8] Klutke G A,Kiessler P C,Wortman M A.A critical look at the bathtub curve[J].IEEE Transactions on Reliability,2003,52(1):125-129.
[9] Zhang G F. Optimum test localization/selection in a diagnostic/prognostic architecture[D].Atlanta(USA):Georgia Institute of Technology,2005.
[10] Tian Zhigang,Zuo M J,Wu Siyan. Crack propagation assessment for spur gears using model-based analysis and simulation[J].Journal of Intelligent Manufacturing,2012,23(2):239-253.
[11] Tan Xiaodong,Qiu Jing,Liu Guanjun,et al.A fault state recognition method based on wavelet energy entropy and hidden semi-Markov models and its applications[J].Mechanical Science and Technology for Aerospace Engineering,2009,28(10):1340-1343.(in Chinese)
谭晓栋,邱静,刘冠军,等.基于小波能谱熵-隐半马尔可夫模型的故障识别方法及应用[J].机械科学与技术,2009,28(10):1340-1343.
[12] Chen Zaigang,Shao Yimin.Dynamic simulation of spur gear with tooth root crack propagating along tooth width and crack depth[J].Engineering Failure Analysis,2011,18:2149-2164.

备注/Memo

备注/Memo:
Received:2015-01-25;Revised:2015-05-22;Accepted:2015-06-15
Foundation:The Specialized Research Fund for the Doctoral Program of Higher Education(20133219110027)
Corresponding author:Senior engineer Fu Pengcheng.E-mail:fang820729@sina.com
Citation:Fu Pengchen,Wang Changming,Duan Junbin,et al.A feature selection for fault prognosis based on fault evolution analysis[J]. Journal of Shenzhen University Science and Engineering, 2015, 32(4): 434-440.(in Chinese)
基金项目:高等学校博士学科点专项科研基金资助项目(20133219110027)
作者简介:傅鹏程(1972—),男(汉族),山西省阳泉市人,南京理工大学博士研究生,北京特种机电技术研究所高级工程师.E-mail:fang820729@sina.com
引文:傅鹏程,王昌明,段俊斌,等.基于故障演化分析的齿轮故障预测特征选择[J]. 深圳大学学报理工版,2015,32(4):434-440.
更新日期/Last Update: 2015-06-30