Based onK-SVDDictionary learning algorithm of sparse said vibration signal compression measurement Reconstruction Methods

  • Mingzhi Zhang
Ariticle ID: 195
860 Views, 12 PDF Downloads
Keywords: Vibration signal, over-complete dictionary, sparse representation, compression perception, Accurate

Abstract

For the current mechanical vibration signal band more and more wide basis traditional Shannon-In quest sampling theorem data collection of an arcane will get big vibration data the storage, transmission and processing bring difficult of problem put forward. Based onK-SVDDictionary learning algorithm of sparse said vibration signal compression measurement reconstruction methods. First analysis the vibration signal in based onK-Singular Value Decomposition(K-Singular Value decomposition K-SVD)Dictionary learning algorithm get of over-complete dictionary on the approximate sparse of CAN compression; then use Gaussian random matrix of vibration signal the compression measurement; finally based on compression measurements the orthogonal Matching Pursuit algorithm the original vibration signal the reconstruction. Simulation Test results show that when vibration signal compression ratio in60%~90%When based onK-SVDDictionary learning algorithm structure of over-complete dictionary than based on discrete cosine over-complete Dictionary Compression sensing reconstruction relative error small. The methods not only can get is high signal compression ratio and has accurate of Signal Reconstruction performance in don't lost vibration information of situation under greatly reduce the original vibration data.

References

Cai Weiwei, Tang Baoping, Huang qingqing.Design of wireless sensor network node for Mechanical Vibration Signal Acquisition[J].Vibration and impact,2013,32 (1):73-78.

Donoho D. Compressed Sensing [J]. IEEE transaction on Information Theory,2006,52 (4):1289-1306.

Candes E,Romberg J,Tao t. Robust uncertainty principles:Exact Signal reconstruction from highly

CANDES EWakin m. An introduction. compressive sampling [J]. IEEE Signal Processing Magazine2008 25 (2):21-30.

Shi brilliant Liu Dan high Dahua and.Compression Perception Theory and Its Research Progress[J].Electronic Journal,200937 (5):1070-1081.

Rauhut HSchnass KVandergheynst P.Compressed Sensing, redundant dictionaries [J]. IEEE Trans Inform Theory200854 (5):2210-2219.

Wang light bed Perlin Wu Dinghai and.Based on lifting wavelet of mechanical vibration signal adaptive compression perception[J].Central South University Journal,201647 (3):771-776.

Guo jun feng of jian xu Ray Chun-Li and.A rolling bearing vibration signal of Data Compression collection of methods[J].Vibration and impact,201534 (23):8-13.

Lee S JLUAN JChou p h. ECG Signal reconstruction from undersampled measurement using a trained overcomplete dictionary [J]. Contemporary Engineering Science20147/(29):1625-1632.

Doneva MBornert PEGGERS HEt A1. Compressed Sensing reconstruction. magnetic resonance parameter mapping [J]. Magnetic Resonance. Medicine201064 (4):1114-1120.

Sun she Yang Zhen season so and.Based on over-complete linear prediction Dictionary of compression perception voice reconstruction[J].Instrument instrument Journal,201233 (4):743-749.

Peng East Zhang Hua Liu ji zhong.Based on over-complete Dictionary of body area network compression perception ECG Reconstruction[J].Automation Journal,201440 (7):1421-1432. PENG XiangdongZHANG HuaLIU jizhong. ECG

WU Jian Ning Xu Haidong was Frank Wang Jue.Based on over-complete dictionary sparse representation of multi-channel EEG Signal Compression perception combined with reconstruction[J].Electronic and information Journal,201638 (7):1666-1673.

Wang Qiang bed Perlin Wang light and.Based on Sparse Decomposition of vibration signal Data Compression Algorithm[J].Instrument instrument Journal,201637 (11):2497-2505.

Yu fajun, Zhou fengxing, Yan Baokang.Sparse Feature Extraction for early fault of Bearings Based on dictionary Learning[J].Vibration and impact,2016,35 (6):186-181. Yu fajun,Zhou fengxing,Yan Baokang. Bearing initial Fault Feature Extraction via sparse representation based on dictionary learning [J]. Journal of vibration and shock,2016,35 (6):181-186.

Candes E,Eldarb Y C,Needella d,Et al. Compressed Sensing with heritage and redundant dictionaries [J]. Applied and computational Harmony Analysis,2011,31 (1):59-73.

Tropp J,Gilbert a C. signal recovery from random Measurements via orthogonal Matching Pursuit [J]. IEEE Trans inform Theory,2007,53 (12),4655-4666.

Aharon m,Elad m,Bruckstein A. K-SVD:An Algorithm for designing overcomplete dictionaries Sparse representation [J]. IEEE Transactions on Signal Processing,2006,54 (11):4311-4322.

Engan K,Aase s o,Husoy j h. Method of Optimal Directions for Frame Design [c]/Procedures of the 1999 IEEE International Conference on Acoustics,Speech,And signal processing. Phoenix:AZ,1999:2443-2446.

Wang Pengfei, Wang Xinqing, Cao Lei, et al.Bearing Based on discriminative Sparse Coding Fault Diagnosis Method[J].Instrumentation Technology and sensors,2016 (8):77-80. Wang Pengfei,Wang Xinqing,Cao Lei,[J]. Instrument Technology and sensor,2016 (8):77-80.

Section
Articles