LIU Feng, WU Xing, PAN Nan, et al. IMPROVED TIME DOMAIN BLIND DECONVOLUTION ALGORITHM IN BEARING FAULT DIAGNOSIS. [J]. 38(2):207-214(2016)
DOI:
LIU Feng, WU Xing, PAN Nan, et al. IMPROVED TIME DOMAIN BLIND DECONVOLUTION ALGORITHM IN BEARING FAULT DIAGNOSIS. [J]. 38(2):207-214(2016) DOI: 10.16579/j.issn.1001.9669.2016.02.001.
IMPROVED TIME DOMAIN BLIND DECONVOLUTION ALGORITHM IN BEARING FAULT DIAGNOSIS
In order to extract fault feature of signal. An improved blind deconvolution algorithm which based on generalized morphological filtering and improved KL distance clustering methods was proposed to deal with industrial field noise,multi interference sources and disadvantage of blind extraction algorithm. First,the generalized morphological filter was used to extract the characteristic signal of observation signal. Then,the orthogonal matching pursuit algorithm was used to remove the period component of signal after being filtered. Finally,the improved KL distance was used to calculate distance of each component and obtain the separated signal by fuzzy C cluster. The results of computer simulation and real rolling bearing signals analysis show that this proposed method is quite effective.
关键词
广义形态滤波压缩感知改进KL距离盲信号处理
Keywords
Generalized morphological filteringCompressed sensingImproved KL distanceBlind signal processing
NEW METHOD FOR BEARING INTELLIGENT DIAGNOSIS BASED ON COMPRESSED SENSING AND MULTILAYER EXTREME LEARNING MACHINE
n-FFT COMPRESSED SENSING ALGORITHM OF SMART DIMENSIONALITY REDUCTION METHOD AND ITS APPLICATION IN FEATURE EXTRACTION AND CLASSIFICATION OF GEAR SYSTEM
Related Author
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Related Institution
School of Mechanical Engineering,University of Dalian
College of Engineering,University of Shantou
School of Electromechanical Vehicle Engineering,Zhengzhou Institute of Technology