An improved initialization method of D-KSVD algorithm for bearing fault diagnosis
An improved initialization method of D-KSVD algorithm for bearing fault diagnosis
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轴承故障诊断D-KSVD算法改进初始化方法
DOI：
10.1007/s12206-017-1010-7
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发表时间：
2017-11
影响因子：
1.6
通讯作者：
Dong Guangming
中科院分区：
文献类型：
--
作者：
Yuan Haodong;Chen Jin;Dong Guangming
提出了一种基于小波包量化特征提取和基于改进的K-SVD初始化算法的模式识别相结合的轴承故障诊断方法。在D-KSVD算法中，字典的表示能力和分类器的区分能力受到其初始值的严重影响。为此，提出了改进的D-KSVD初始化法，并将其应用于轴承故障诊断。改进方法是在训练阶段的初始化阶段，利用K-SVD分别对每个类别对应的子字典进行训练，然后通过级联子字典来构建能够完全代表所有类别特征的初始字典，而对于线性分类器的初始化则采用朴素贝叶斯分类器。实验结果表明，在参数相同的情况下，改进的D-KSVD比传统的D-KSVD等分类方法具有更好的分类能力。
A novel bearing fault diagnosis method combining feature extraction based on wavelet packets quantifiers and pattern recognition method based on improved initialization method of Discriminative K-SVD (D-KSVD) algorithm is proposed. In D-KSVD algorithm, the representational power of dictionary and discriminative ability of classifier are seriously affected by their initialization values. Therefore, the improved initialization method of D-KSVD is presented and employed for bearing fault diagnosis. The improvement is that during the initialization of training stage, subdictionaries corresponding to each category are trained by K-SVD separately and then the initial dictionary is constructed by cascading the subdictionaries, which can completely represent the characteristics of all categories, and as for the initialization of linear classifier, naive Bayesian classifier is utilized. The experimental results show that under the same parameters the improved D-KSVD has better classification ability compared with traditional D-KSVD and some other classification methods.
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DOI：
10.1201/b10345-5
发表时间：
2010-11
期刊：
Encyclopedia of Autism Spectrum Disorders
影响因子：
--
作者：
Kim-Anh Lê Cao;Z. Welham
通讯作者：
Kim-Anh Lê Cao;Z. Welham
DOI：
10.1002/047134608x.w5513.pub2
发表时间：
2019-02
期刊：
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影响因子：
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作者：
K. Kulkarni;P. Turaga;Anuj Srivastava;Rama Chellappa
通讯作者：
K. Kulkarni;P. Turaga;Anuj Srivastava;Rama Chellappa
影响因子：
3.9
作者：
Lorenzo Vaquero;V. Brea;M. Mucientes
通讯作者：
Lorenzo Vaquero;V. Brea;M. Mucientes
DOI：
10.1049/iet-bmt.2018.5117
发表时间：
2016
期刊：
IET Biom.
影响因子：
--
作者：
Sheng He;Lambert Schomaker
通讯作者：
Sheng He;Lambert Schomaker
DOI：
--
发表时间：
2007
期刊：
--
影响因子：
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作者：
Roger B. Grosse;Helen Kwong
通讯作者：
Roger B. Grosse;Helen Kwong