基于小波包字典优化的旋转机械振动信号压缩感知重构方法

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振动与冲击 ›› 2018, Vol. 37 ›› Issue (22) : 164-172.

论文

温江涛1，孙洁娣 2，于洋1，闫常弘1

作者信息 +

Compressed Sensing reconstruction forrotating machinery vibration signals based on the wavelet packet dictionary optimization

WEN Jiangtao 1 SUN Jiedi 2, YU Yang1 YAN Changhong1

Author information +

文章历史 +

摘要

采用工业无线传感器网络的机械状态监测系统需要进行复杂的数据压缩和高精度的重构，而传感器网络节点资源受限，针对这一问题本文提出基于小波包字典优化的旋转机械振动信号压缩感知重构方法。该方法首先结合小波包多分辨率分析及K-SVD字典训练方法，提出了小波包字典优化方法代替传统的正交基字典稀疏表示方法，提高稀疏度。其次根据旋转机械振动信号自身特征，提出用块稀疏贝叶斯学习最大期望值算法，代替传统仅依赖于稀疏假设的算法实现信号重构。实际轴承振动信号仿真结果表明，该方法相对于传统的压缩感知方法重构性能明显提高。

Abstract

Machinery condition monitoring systems using industrial wireless sensor networks need complicated data compression and high-precisionreconstruction,however, thereare some limitations in the node resources of wireless sensor networks.A compressed sensing reconstruction method for rotating machinery vibration signals was proposed based on the wavelet packet dictionary optimization.Combining the multiresolution analysis with the K-SVD dictionary training, the wavelet packet dictionary optimization was introduced to replace the traditional sparse transformation method based on the orthogonal basis dictionary for improving the signal sparseness.According to the rotating machinery vibration signal characteristics, a block sparse Bayesian learning framework was put forward in which the expectation-maximization method was applied instead of the common reconstruction algorithms only based on the sparsity assumption.The experimentalresults show the proposed method has better reconstruction performance than traditional methods.

导出引用

温江涛1，孙洁娣 2，于洋1，闫常弘1. 基于小波包字典优化的旋转机械振动信号压缩感知重构方法[J]. 振动与冲击, 2018, 37(22): 164-172

WEN Jiangtao 1 SUN Jiedi 2, YU Yang1 YAN Changhong1 . Compressed Sensing reconstruction forrotating machinery vibration signals based on the wavelet packet dictionary optimization[J]. Journal of Vibration and Shock, 2018, 37(22): 164-172

参考文献

[1] 陈雪峰, 曹宏瑞, 何正嘉. 数字化制造装备的主轴服役性能监测与诊断[J]. 机械工程学报, 2009, 45(05): 140-146.
CHENXuefeng, CAO Hongrui, HE Zhengjia. Monitoring and Diagnosis of Spindle Service Performance of Digitized Manufacturing Equipment [J]. Journal of Mechanical Engineering, 2009, 45(05): 140-146.
[2] RANDALL R B, ANTONI J. Rolling element bearing diagnostics-A tutorial[J]. Mechanical Systems and Signal Processing, 2011, 25(2): 485-520.
[3] EL-THALJI I, JANTUNEN E. A summary of fault modelling and predictive health monitoring of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2015, 60-61: 252-272.
[4] 蔡巍巍, 汤宝平, 黄庆卿. 面向机械振动信号采集的无线传感器网络节点设计[J]. 振动与冲击, 2013, 32(1):73-77.
CAI Weiwei, TANG Baoping，HUANG Qingqing. Design of wireless sensor network node for collecting mechanical vibration signals [J]. Journal of vibration and shock, 2013, 32(1):73-77.
[5] 林蔚, 韩丽红. 无线传感器网络的数据压缩算法综述[J]. 小型微型计算机系统, 2012, 33(09): 2043-2048.
LIN Wei, HAN Lihong. Summary of Data Compression Algorithm in Wireless Sensor Networks[J]. Journal of Chinese Computer Systems, 2012, 33(09): 2043-2048.
[6] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[7] ZHOU Z H, ZHAO J W, CAO F L. A novel approach for fault diagnosis of induction motor with invariant character vectors [J]. Information Sciences, 2014, 281: 496-506.
[8] ZHANG X P, HU N Q, HU L, et al. A bearing fault diagnosis method based on the low-dimensional compressed vibration signal[J]. Advances in Mechanical Engineering, 2015, 7(7): 12.
[9] WANG Y X, XIANG J W, MO Q Y, et al. Compressed sparse time-frequency feature representation via compressive sensing and its applications in fault diagnosis[J]. Measurement, 2015, 68: 70-81.
[10] WANG H Q, KE Y L, LUO G G, et al. A Two-Stage Compression Method for the Fault Detection of Roller Bearings[J]. Shock and Vibration, 2016(4) :1-11.
[11] DU Z H, CHEN X F, ZHANG H, et al. Feature Identification With Compressive Measurements for Machine Fault Diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2016, 65(5): 977-987.
[12] 贾民平, 杨建文. 滚动轴承振动的周期平稳性分析及故障诊断[J]. 机械工程学报, 2007, 43(01): 144-146+151.
JIA Pingmin, YANG Jianwen. CYCLIC-STATIONARY ANALYSIS OF BEARING VIBRATION AND FAULT DIAGNOSIS[J]. Journal of Mechanical Engineering, 2007, 43(01): 144-146+151.
[13] 康晨晖, 崔玲丽, 王婧, 等. 基于信号特征的复合字典多原子匹配算法研究[J]. 机械工程学报, 2012, 48(12): 1-6.
KANG Chaohui，CUI Lingli, WANG Jing, et al. Research on the Composite Dictionary Multi-atoms Matching Algorithm Based on the Signal Character [J]. Journal of Mechanical Engineering, 2012, 48(12): 1-6.
[14] BARANIUK R G. Compressive sensing [J]. IEEE Signal Processing Magazine, 2007, 24(4): 118-121+124.
[15] QAISAR S, BILAL R M, IQBAL W, et al. Compressive Sensing: From Theory to Applications, a Survey [J]. Journal of Communications and Networks, 2013, 15(5): 443-456.
[16] ELDAR Y C, KUPPINGER P, BOLCSKEI H. Block-Sparse Signals: Uncertainty Relations and Efficient Recovery[J]. IEEE Transactions on Signal Processing, 2010, 58(6): 3042-3054.
[17] ZHANG Z L, JUNG T P, MAKEIG S, et al. Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning[J]. IEEE Transactions on Biomedical Engineering, 2013, 60(2): 300-309.
[18] ZHANG Z L, JUNG T P, MAKEIG S, et al. Compressed Sensing of EEG for Wireless Telemonitoring With Low Energy Consumption and Inexpensive Hardware [J]. IEEE Transactions on Biomedical Engineering, 2013, 60(1): 221-224.
[19] ZHANG Z L, RAO B D. Extension of SBL Algorithms for the Recovery of Block Sparse Signals With Intra-Block Correlation [J]. IEEE Transactions on Signal Processing, 2013, 61(8): 2009-2015.
[20] Aharon M, Elad M, Bruckstein A. K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation [J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.
[21] Case Western Reserve University.
http://csegroups.case.edu/bearingdatacenter/home.
[22] TROPP J A, GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit [J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666.
[23] EFRON B, HASTIE T, JOHNSTONE I, et al. Least angle regression [J]. Annals of Statistics, 2004, 32(2): 407-451.
[24] BREDIES K, LORENZ D A. Linear Convergence of Iterative Soft-Thresholding [J]. Journal of Fourier Analysis and Applications, 2008, 14(5-6): 813-837.