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