基于改进频域压缩感知的轴承复合故障欠定盲提取

PDF(3073 KB)

振动与冲击 ›› 2015, Vol. 34 ›› Issue (14) : 123-128.

论文

周俊，伍星，迟毅林，潘楠

作者信息 +

Underdetermined blind extraction for bearings combined failures based on the improved frequency domain compression sense

ZHOU Jun，WU Xing，CHI Yi-lin，PAN Nan

Author information +

文章历史 +

摘要

针对旋转机械复合故障频域盲提取算法的不足，为提高欠定盲提取分离结果精度，提出基于多尺寸多结构元素的闭-开组合形态滤波、遗传模拟退火聚类及频域压缩感知重构算法相结合的(C-OACMF-GASA-CS)故障特征欠定盲提取方法。利用形态滤波滤除背景噪声及提取冲击信号；利用遗传模拟退火算法估计混合矩阵；用估计矩阵重构传感矩阵，并用正交匹配追踪基频域压缩感知重构分离信号。实验仿真及双通道滚动轴承故障加速度振动信号分析结果表明，该方法能有效分离提取滚动轴承故障特征。

Abstract

Aiming at the lack of frequency-domain blind extraction algorithm for complex rotating machinery fault, and improving the separation results accuracy of underdetermined blind extraction, a method based on multi-scale multi- structure close-open average combination morphological filtering(C-OACMF), genetic simulated annealing clustering (GASA) and frequency domain compressive sensing algorithm(CS) was proposed to deal with underdetermined blind fault feature extraction . First, the C-OACMF was used to filter out background noise and extract the shock signals; then, using the GASA of fuzzy C-average clustering algorithm to estimate the mixing matrix; finally the sensor matrix was remodeled by the estimation matrix and the orthogonal matching pursuit of frequency domain CS algorithm was used to estimate the source signals .The results of computer simulation and real rolling bearing signals analysis show that this proposed method is quite effective.

导出引用

周俊，伍星，迟毅林，潘楠. 基于改进频域压缩感知的轴承复合故障欠定盲提取[J]. 振动与冲击, 2015, 34(14): 123-128

ZHOU Jun，WU Xing，CHI Yi-lin，PAN Nan . Underdetermined blind extraction for bearings combined failures based on the improved frequency domain compression sense[J]. Journal of Vibration and Shock, 2015, 34(14): 123-128

参考文献

[1] 何正嘉,陈进,王太勇,等.机械故障诊断理论及应用[M].北京:高等教育出版社,2010.
[2] 沈路,周晓军,张文斌,等. 基于形态滤波与灰色关联度的滚动轴承故障诊断[J].振动与冲击,2009, 28(11): 17-20.
SHEN Lu, ZHOU Xiao-jun, ZHANG Wen-bin, et al. Fault diagnosis of rolling element bearing based on morphological filter and grey incidence[J]. Journal of Vibration and Shock, 2009, 28(11): 17-20.
[3] 郝如江,卢文秀,褚福磊.形态滤波在滚动轴承故障声发射信号处理中的应用[J].清华大学学报(自然科学版),2008,48(5): 812-815.
HAO Ru-jiang, LU Wen-xiu,CHU Fu-lei. Morphology filters for analyzing roller bearing fault using acoustic emission signal processing[J]. Journal of Tsinghua University (Science and Technology), 2008,48(5):812-815.
[4] 李豫川,伍星,迟毅林,等.基于形态滤波和稀疏分量分析的滚动轴承故障盲分离[J].振动与冲击,2011,30(12):170-174.
LI Yu-chuan, WU Xing, CHI Yi-lin, et al. Blind separation for rolling bearing faults based on morphological filtering and sparse component analysis [J]. Journal of Vibration and Shock, 2011,30(12):170-174.
[5] 潘楠,伍星,迟毅林,等.欠定盲解卷积用于滚动轴承复合故障声学诊断[J].振动、测试与诊断,2013,33(2):284-289.
PAN Nan,WU Xing,CHI Yi-lin,et al.
Journal of Vibration，Measurement & Diagnosis, 2013, 33(2): 284-289.
[6] 余丰,奚吉,张力,等.基于CS与K-SVD的欠定盲源分离稀疏分量分析[J].东南大学学报(自然科学版)，2011,41(6):1127-1131.
YU Feng, XI Ji, ZHANG Li, et al. Sparse presentation of underdetermined blind source separation based on compressed sensing and K-SVD [J]. Journal of Southeast University (Natural Science Edition), 2011, 41(6):1127-1131.
[7] 李丽娜,曾庆勋,甘晓晔,等.基于势函数与压缩感知的欠定盲源分离[J].计算机应用,2014,34(3):658-662.
LI Li-na, ZENG Qing-xun, GAN Xiao-ye, et al. Under- determined blind source separation based on potential function and compressive sensing [J]. Journal of Computer Applications, 2014,34(3):658-662.
[8] 孙敬敬.数学形态学在振动信号处理中的应用研究[D].北京:华北电力大学,2012.
[9] 沈路. 数学形态学在机械故障诊断中的应用[D]. 浙江:浙江大学,2010.
[10] 史峰,王辉,郁磊,等. Matlab 智能算法30个案例分析[M].北京:北京航空航天大学出版社,2011.
[11] 刘冰.压缩感知框架下信号检测与参数估计算法研究[D].哈尔滨:哈尔滨工业大学,2012.