轴承损伤是机械设备损伤的主要原因之一,其产生的振动信号具有微弱、非平稳和非线性的特点。针对不能准确从微弱信号中提取故障特征的问题,提出使用多尺度子带样本熵,首先对信号进行小波包分解得到多尺度信号,再将每一个多尺度信号进行子带分解得到多尺度子带信号,再求其样本熵得到多尺度子带样本熵,该方法能深入挖掘微弱信号的本质特征;针对非平稳信号能量密度分布不均的问题,提出使用平滑伪Wigner-Ville分布,其可对非平稳信号的瞬时对称相关函数进行时频聚集处理,使信号的能量均匀分布;针对不能准确的挖掘非线性数据的主流形的问题,提出使用局部保持投影(LPP,Locality Preserving Projection),LPP在投影过程中保持了最优的数据局部邻域关系,可以准确的挖掘非线性数据的主流形。文中分别采用四组正常、内圈故障、滚珠故障和外圈故障信号作为原始数据来验证该方法的有效性,实验结果证明该方法能有效地对信号故障进行分离和识别。
Abstract
One of the primary cause of malfunction generated by machinery is bearing damage, and its vibration signal has the characteristics of weak, non-stationary and nonlinear. It proposes multi-scale sub-band sample entropy concept aiming at the problem of eigenvalues and eigenvectors that can’t be accurately extracted from the weak signals, firstly, obtaining the multi-scale signal by wavelet packet decomposed, and then, sub-band decomposing each signal of the scales, finally, solving the sample entropy of each sub-band, this method can dig deep into the essential characteristics of the weak signal. It presents that adopting smooth pseudo Wigner-Ville to solve the problem of uneven distribution of energy density of non-stationary signal, it can be used to deal with the time and frequency aggregation of the instantaneous symmetric correlation function of non-stationary signal, make the signal energy is evenly distributed. It proposes that using LPP (Locality Preserving Projection) decomposition to settle the problem of can’t accurate image of the mainstream of the nonlinear data, LPP in the process of projection to keep the relationship between the optimal data of the local neighborhood, the main manifold can be accurately excavated from nonlinear data. The paper adopting a group of normal, inner ring fault, balls fault and outer ring fault signal as the original data to verify this method’s validity, he experimental results prove that this method can effectively to separation and identification of signal failure.
关键词
轴承损伤 /
特征提取 /
多尺度子带样本熵 /
平滑伪Wigner-Ville分布 /
LPP分布
{{custom_keyword}} /
Key words
Bearing damage /
Feature extraction /
Multi-scale sub-band sample entropy /
Smoothed Pseudo Wigner-Ville distribution /
LPP decomposition
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Seung H S,Daniel D L.The manifold ways of perception[J].Science(S0036-8075),2000,290(5500):2268-2269.
[2] Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation[J].NeuralComputation.2003,15(6):1373-1396.
[3]Roweis S, Saul L.Nonlinear dimensionality reduction by locally linear embedding[J]. Science(S0036-8075),2000,290(5500):2323-2326.
[4]ZHANG Zhen-yue,Zha Hong-yuan. Principal manioflds and nonlinear dimension reduction via Tangent Space Alignmnet[J]. SIAM Journal on Scientific Computing,2005,26(1):313-338.
[5]丁晓喜,何清波.基于WPD和LPP的设备故障诊断方法研究[J].振动与冲击,2014,33(3):89-93.
DING Xiao-xi, He Qing-bo. Machine fault diagnosis based on WPD and LPP[J]. Journal of vibration and shock, 2014,33(3):107-110.
[6]张晓涛.基于多尺度正交PCA-LPP流形学习算法的故障特征增强方法[J].振动与冲击,2015,34(13):66-70.
ZHANG Xiao-tao. Fault feature enhancement method based on multi-scale orthogonal PCA-LPP manifold learning algorithm[J].Journal of vibration and shock, 2015,34(13):66-70.
[7]李国芳.基于2DPCA和流形学习LPP算法的人脸特征提取应用[J].电脑知识与技术,2014,10(31):7438-7441.
LI Guo-fang, Face feature extraction based on 2DPCA and LPP manifold learning algorithm[J].Computer Knowledge and Technology,2014,10(31):7438-7441.
[8]郑近德,程军圣,胡思宇.多尺度熵在转子故障诊断中的应用[J].振动、测试与诊断,2013,33(2):294-297.
ZHENG Jin-de, CHENG Jun-sheng, HU Si-yu. Rotor fault diagnosis based on multi-scale entropy[J]. Journal of vibration, Measurement&Diagnosis,2013,33(2):294-297.
[9] 臧怀刚,王石云,李玉奎.EMD和平滑伪wigner-ville谱熵的轴承故障诊断[J]. 噪声与振动控制,2014,34(5):145-149.
ZANG Huai-gang, WANG Shi-yun, LI Yu-kui. Bearing fault diagnosis based on EMD and smoothed pseudo wigner-ville spectrum entropy[J]. Noise and Vibration Control,2014,34(5):145-149.
[10]Richman J S, J Randall Moorman. Physiological Time Series Analysis Using Approximate Entropy and Sample Entropy[J]. Am J Physiol Circ Heart Physio,2000,278:2039-2049.
[11]齐晓轩,郭婷婷,贾志勇.基于Fast-ICA的wigner-ville分布交叉项消除方法[J].计算机工程,2015,41(8):71-75.
QI Xiao-xuan, GUO Ting-ting, JIA Zhi-yong. Approach of eliminating cross-term of wigner-ville distribution based on Fast-ICA[J]. Computer Engineering,2015,41(8):71-75.
[12]Gupta S, Degrande G, Lombaert G.Experimental Validation of a Numerical Model for Subway Induced Vibrations[J]. Journal of Sound and Vibration, 2009,321(3-5):786-812.
[13]乐叶青.基于wigner-ville分布的电能质量扰动的分析[D].浙江:浙江大学,2007:11-21.
LE Ye-qing. The Analysis of power quality disturbance based on wigner-ville distribution[D]. ZheJiang:Zhejiang university,2007:11-21.
[14]LI Wei, Prasad S, Fowler J E,et al. Locality-Preserving dimensionality reduction and classification for hyperspectral image analysis[J]. IEEE Transactions on Geoscience and Remote Sensing,2012,50(4):1185-1198.
[15]LU Gui-fu, LIN Zhong, JIN Zhong. Orthogonal complete discriminant locality preserving projections for face recognition[J].Neural Processing Letters,2011,33(3):235-250.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}