GS-ASTFA方法及其在滚动轴承寿命预测中的应用

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (11) : 14-19.

论文

欧龙辉，彭晓燕，杨宇，程军圣

作者信息 +

GS-ASTFA Method and Its Application to life prediction of rolling bearing

OU Long-hui, PENG Xiao-yan, YANG Yu, CHENG Jun-sheng

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文章历史 +

摘要

自适应最稀疏时频分析（Adaptive and Sparsest Time-Frequency Analysis ，简称ASTFA）方法是一种新的信号分解方法，该方法将信号分解问题转化为优化问题，以得到信号的最稀疏解。优化过程采用高斯-牛顿迭代算法，但高斯-牛顿迭代算法对初值依赖性高，本文采用黄金分割法（Golden Section，简称GS）对ASTFA方法进行初值搜索，提出了基于黄金分割搜索初值的ASTFA方法（简称GS-ASTFA），仿真信号的分析结果验证了改进方法的有效性。继而采用该方法提取了滚动轴承故障特征值，并成功地进行了故障特征值趋势分析和寿命预测。

Abstract

Adaptive and Sparsest Time-Frequency Analysis (ASTFA) is a new method of signal decomposition. In order to get the sparsest solution of the signal, ASTFA translate signal decomposition into optimization problem. In the optimization procedure, Gauss - Newton iterative algorithm is adopted. However, Gauss - Newton iterative algorithm is sensitive to the choice of initial value. In this paper, the Golden Section (GS) was applied to searching initial value and Golden Section based ASTFA (GS-ASTFA) method are proposed in this paper. The simulation results show that the proposed approach is valid. Furthermore, GS-ASTFA method is applied to rolling bearing eigenvalue extraction, eigenvalue trend analysis and life prediction.

导出引用

欧龙辉，彭晓燕，杨宇，程军圣. GS-ASTFA方法及其在滚动轴承寿命预测中的应用[J]. 振动与冲击, 2017, 36(11): 14-19

OU Long-hui, PENG Xiao-yan, YANG Yu, CHENG Jun-sheng. GS-ASTFA Method and Its Application to life prediction of rolling bearing[J]. Journal of Vibration and Shock, 2017, 36(11): 14-19

参考文献

[1] Runqing Huang; Lifeng Xi; Xinglin Li; C. Richard Liu; Hai Qiu; Jay Lee. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods [J]. Mechanical Systems and Signal Processing, 2007,21:193–207.
[2] Francesco Di Maio; Kwok Leung Tsui; Enrico Zio. Combining Relevance Vector Machines and exponential regression for bearing residual life estimation[J]. Mechanical Systems and Signal Processing,2012,31:405–427.
[3] Yu Dejie; Cheng Junsheng; Yang Yu. Fault diagnosis approach for roller bearings based on empirical mode decomposition method and Hilbert transform[J]. Chinese Journal of Mechanical Engineering,2005, 18(2):267-270.
[4] 张绍辉;李巍华.基于特征空间降噪的局部保持投影算法及其在轴承故障分类中的应用[J]. 机械工程学报, 2014, 50(3):92-99.
ZHANG Shaohui; LI Weihua. Locality Preserving Projections Based on Feature Space Denoising and Its Application in Bearing Fault Classificaiton[J]. 2014, 50(3):92-99.
[5] Dong Wang; Qiang Miao; Xianfeng Fan; Hong-Zhong Huang. Rolling element bearing fault detection using an improved combination of Hilbert and Wavelet transforms[J]. Journal of Mechanical Science and Technology, 2009, Vol.23 (12):3292-3301.
[6] 程军圣;于德介;杨宇.基于EMD的能量算子解调方法及其在机械故障诊断中的应用[J].机械工程学报, 2004,40(8):115-118.
Cheng J S，Yu D J，Yang Y. Energy operator demodulating approach based on EMD and its application in mechanical fault diagnosis[J]．Chinese Journal of Mechanical Engineering, 2004, 40(8):115-118.
[7] HAN Jian-gang;REN Wei-xin;SUN Zeng'-shou．Wavelet packet based damage identification of beam structures[J]．International Journal of Solids and Structures，2005，42(26)：6610—6627．
[8] Z. Wu and N. E. Huang. Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis. 2009,1(1) : 1-41.
[9] Zhongjie Shen;Xuefeng Chen;Xiaoli Zhang;et al. A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM[J]. Measurement, 2012, 45(1): 30-40.
[10] THOMAS Y; Hou, Zuoqiang;SHI. Adaptive data analysis via sparse time-frequency representation[J]. Advances in Adaptive Data Analysis, 2011,3(1, 2): 1-28.
[11] Thomas Y;Hou Zuoqiang;SHI. Data Driven Time-Frequency Analysis[J]. Applied and Computational Harmonic Analysis,2013,35(2):284-308.
[12] 程军圣;彭延峰;杨宇;刘燕飞. 自适应最稀疏时频分析与经验模态分解的对比研究[A]. 2014年全国设备监测诊断与维护学术会议、第十六届全国设备监测与诊断学术会议、第十四届全国设备故障诊断学术会议暨2014年全国设备诊断工程会议论文集[C].振动与冲击，2015,4,30:523-525.
CHENG Jun-sheng; PENG Yan-feng; YANG Yu; LIU Yan-fei. Comparison between Adaptive and Sparsest Time-frequency Analysis and Empirical Mode Decomposition[A]. Journal of Vibration and Shock, 2015, 4, 30:523-525.
[13] 洪加威.论黄金分割法的最优性[J].数学的实践与认识，1973,2:34-41.
Hong Jiawei. The optimality of golden section method[J]. Mathematics In Practice and Theory, 1973,2:34-41.
[14] Hai Qiu, Jay Lee, Jing Lin. Wavelet Filter-based Weak Signature Detection Method and its Application on Roller Bearing Prognostics. Journal of Sound and Vibration 289 (2006): 1066-1090
[15] 李玉庆;王日新;徐敏强;周瑞生.针对滚动体损伤的滚动轴承损伤严重程度评估方法[J]. 振动与冲击, 2013, 32(18):169-173.
LI Yu-qing;WANG Ri-xin;XU Min-qiang;ZHOU Rui-sheng. A damage severity assessment method for beatings with rolling element damage[J]. Journal of Vibration and Shock, 2013, 32(18):169-173.
[16] 胡爱军;马万里;唐贵基.基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法[J]. 中国电机工程学报, 2012, 32(11):106-111.
Hu Aijun;Ma Wanli;Tang Guiji. Rolling Bearing Fault Feature Extraction Method Based on Ensemble Empirical Mode Decomposition and Kurtosis Criterion[J]. Proceedings of the CSEE, 2012, 32(11):106-111.