Incremental dictionary learning for fault detection with applications to oil pipeline leakage detection

被引：14

作者：

Yan, J. C. ^{[1
]}

Tian, C. H. ^{[1
]}

Huang, J. ^{[1
]}

Albertao, F. ^{[1
]}

机构：

[1] IBM Res China, Beijing 100193, Peoples R China

来源：

ELECTRONICS LETTERS | 2011年 / 47卷 / 21期

关键词：

D O I：

10.1049/el.2011.1573

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In the signal processing domain, there has been growing interest in sparse coding with a trained overcomplete dictionary instead of a pre-defined one. Sparse coding is advocated as an effective mathematical description for the underlying principle of human sensory systems. Proposed is a framework for online fault detection with applications to oil pipeline leakage detection. The method first performs supervised offline overcomplete dictionary training using the labelled samples. During the online stage, the dictionary is continuously updated in an incremental fashion to adapt to the varied upcoming samples.

引用

页码：1198 / U61

页数：2

共 6 条

[1] SOME INFORMATIONAL ASPECTS OF VISUAL PERCEPTION [J].