针对目前机械设备越来越智能化、高速化、集成化和复杂化，传统的压缩感知模型（单测量向量）获得的测量信息比较单一，需要在不同监测点分别进行测量获得多个信号数据，浪费时间，并且忽略同一机器不同监测点之间信号的相关性，为充分利用信号间和信号内的相关性，更大程度上减少冗余性和采样时间，提出一种基于多测量向量模型的机械振动信号联合稀疏重构方法。重点研究了重构方法的设计：基于粒子群算法，首先通过时间稀疏贝叶斯算法求解出初始解，然后结合贪婪算法的修剪技巧并加入自适应粒子激活机制进行位置更新寻找最优解，最后对振动信号进行精确重构。实验结果显示，该方法较其它方法而言能有效的恢复机械振动信号且重构误差相对较小。
Abstract
Aiming at mechanical equipment becoming more and more intelligent, high-speed, integrated and complicated, The traditional compressed sensing model (single measurement vector) obtains single measurement information, it needs measuring at different monitoring points to obtain multiple signal data, it wastes time and ignores signal correlation between different monitoring points on the same machine. Here, to make full use of inter-signals and intra-signal correlations and further reduce redundancy and sampling time, a joint sparse reconstruction method for mechanical vibration signals based on the multi-measurement vector model was proposed. The design of the reconstruction method was studied emphatically. Firstly, based on the particle swarm optimization algorithm, the initial solution was solved with the time sparse Bayesian algorithm. Then, the pruning technique of the greedy algorithm combined with the adaptive particle activation mechanism was used to do position updating and search the optimal solution. Finally, the vibration signal was reconstructed accurately. Test results showed that compared with other methods, this method can effectively recover mechanical vibration signal and the reconstruction error is relatively smaller.
关键词
机械振动信号 /
压缩感知 /
多重测量向量 /
重构算法 /
粒子群算法
{{custom_keyword}} /
Key words
mechanical vibration signal /
compressed sensing /
multi-measurement vector /
reconstruction algorithm /
particle swarm optimization (PSO)
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 费晓琪, 孟庆丰, 何正嘉. 基于冲击时频原子的匹配追踪信号分解及机械故障特征提取技术[J]. 振动与冲击, 2003, 22(2): 26-29.
By, Fei Xiaoqi, Meng Qingfeng, He Zhengjia. Matching tracking signal decomposition and mechanical fault feature extraction based on impact time-frequency atom[J]. Vibration and Shock, 2003, 22(2): 26-29.
[2] Donoho D. Compressed Sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[3] Cotter S F, Rao B D, Engan K, et al. Sparse Solutions to Linear Inverse Problems with Multiple Measurement Vectors[J]. IEEE Transactions on Signal Processing, 2005, 53(7):2477-2488.
[4] Van d B E, Friedlander M P. Theoretical and Empirical Results for Recovery from Multiple Measurements[J]. IEEE Transactions on Information Theory, 2010, 56(5): 2516-2527.
[5] Tropp J A, Gilbert A C, Strauss M J. Simultaneous Sparse Approximation via Greedy Pursuit[C]// 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005: 721-724.
[6] Duarte M.F, Eldar Y.C. Structured Compressed Sensing: From Theory to Applications[J]. IEEE Transactions on Signal Processing, 2011, 59(9): 4053-4085.
[7] Eldar Y C, Rauhut H. Average Case Analysis of Multichannel Sparse Recovery Using Convex Relaxation[J]. IEEE Transactions on Information Theory, 2009, 56(1):505-519.
[8] Wipf D P. Bayesian Methods for Finding Sparse Representations[J]. Electronic Theses and Dissertations, 2006: 1-238.
[9] Zhang Z, Rao B D. Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5): 912-926.
[10] P. Stoica and R. Moses, Introduction to Spectral Analysis. Upper Saddle River, NJ: Prentice-Hall, 1997.
[11] Qi C, Wang X, Wu L. Underwater Acoustic Channel Estimation based on Sparse Recovery Algorithms[J]. IET Signal Processing, 2011, 5(8): 739-747.
[12] Ji S, Dunson D, Carin L. Multitask Compressive Sensing[J]. IEEE Transactions on Signal Processing, 2009, 57(1): 92-106.
[13] Xu F, Wang S. A Hybrid Simulated Annealing Thresholding Algorithm for Compressed Sensing[J]. Signal Processing, 2013, 93(6):1577-1585.
[14] 朱丰, 张群, 柏又青, 等. 一种新的基于遗传算法的压缩感知重构方法及其在SAR高分辨距离像重构中的应用[J]. 控制与决策, 2012, 27(11): 1669-1675.
Zhu Feng, ZhangQun, Bai, Youqing, et al. A New Genetic Algorithm Based Compressed Sensing Reconstruction Method and Its Application in SAR High Resolution Range Image Reconstruction[J]. Control and decision, 2012, 27(11): 1669-1675.
[15] 陈永倩, 顾建峰, 肖先赐. 用禁忌搜索实现DOA估计[J]. 电波科学学报, 2005(01): 55-58.
Chen Yongqian, Gu Jianfeng, Xiao Xianci. Implementing DOA Estimation with Tabu Search[J]. Journal of RadioScience, 2005(01): 55-58.
[16] 刘鲁锋, 杜新鹏, 成礼智. 一种基于粒子群优化的稀疏恢复算法[J]. 电子与信息学报, 2013, 35(11): 2733-2738.
Liu Lufeng, Du Xinpeng, Cheng Lizhi. A sparse recovery algorithm based on particle swarm optimization[J]. Electronics and Information Technology, 2013, 35(11): 2733-2738.
[17] Du X, Cheng L, Liu L. A Swarm Intelligence Algorithm for Joint Sparse Recovery[J]. IEEE Signal Processing Letters, 2013, 20(6): 611-614.
[18] 相同. 基于改进量子粒子群算法的压缩感知重构算法及应用研究[D]. 天津师范大学, 2014.
Xing Tong. Research on Compressed Sensing Reconstruction Algorithm Based on Improved Quantum Particle Swarm Optimization Algorithm and Its Application[D].Tianjin Normal University, 2014.
[19] Candes E J, Romberg J, Tao T. Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information[J]. IEEE Transactions on Information Theory, 2004, 52(2): 489-509.
[20] Malioutov D, Cetin M, Willsky A S. A Sparse Signal Reconstruction Perspective for Source Localization with Sensor Arrays[J]. IEEE Transactions on Signal Processing, 2005, 53(8): 3010-3022.
[21] Sun L, Liu J, Chen J, et al. Efficient Recovery of Jointly Sparse Vectors[C]// Advances in Neural Information Processing Systems, 2009: 1812-1820.
[22] Rao B D, Engan K, Cotter S F, et al. Subset Selection in Noise Based on Diversity Measure Minimization[C]// IEEE Transactions on Signal Processing, 2003: 760-770.
[23] Figueiredo M A T, Nowak R D, Wright S J. Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse problems [J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586-597.
[24] Schmidt R O. Multiple Emitter Location and Signal Parameter Estimation[J]. IEEE Transactions on Antennas and Propagation, 1986, 34(3): 276-280.
[25] Eldar Y C, Mishali M. Robust Recovery of Signals from a Structured Union of Subspaces[J]. IEEE Transactions on Information Theory, 2009, 55(11): 5302- 5316.
[26] Kennedy J, Eberhart R. Particle Swarm Optimization[C]// Icnn'95 - International Conference on Neural Networks, IEEE, 2002: 4 1942-1948.
[27] Poli R. Particle Swarm Optimization-An Overview[J]. Swarm Intelligence, 2007, 1(1).
[28] Engelbrecht A. 计算智能导论[M]. 谭营译. 2nd ed. 清华大学出版社, 2010.
Engelbrecht A. Introduction to computational intelligence[M]. Translation by Tan Ying. 2nd ed. Tsinghua University Press, 2010.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(2249 KB)
