匹配追踪
奇异值分解
瞬态(计算机编程)
算法
信号(编程语言)
计算机科学
K-SVD公司
特征提取
残余物
模式识别(心理学)
谐波
人工智能
稀疏逼近
压缩传感
物理
声学
操作系统
程序设计语言
作者
Yi Qin,Jingqiang Zou,Baoping Tang,Yi Wang,Haizhou Chen
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2019-04-04
卷期号:16 (1): 215-227
被引量:99
标识
DOI:10.1109/tii.2019.2909305
摘要
To detect the incipient faults of rotating parts used in electromechanical systems widely, a novel transient feature extraction method based on the improved orthogonal matching pursuit (OMP) and one-dimensional K-SVD algorithm is explored in this paper. First, the stopping criterion of adaptive spark is developed, and then the corresponding OMP algorithm is used to remove the modulated and harmonic signals adaptively. Second, the residual signal is reformulated as a signal matrix by period segmentation and circulating shift, and the initial transient dictionary is constructed via the time-domain average technique. Subsequently, a novel K-SVD algorithm is proposed to get the optimized transient dictionary for the one-dimensional signal. Finally, the repetitive transient signal is recovered by the optimized dictionary. The simulated and experimental results show that the proposed method can not only much faster extract the fault characteristics than the traditional K-SVD method, but also more accurately detect the repetitive transients than the infogram method and the traditional K-SVD method.
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