A cosine similarity-based negative selection algorithm for time series novelty detection

被引：45

作者：

Dong, Yonggui ^{[1
]}

Sun, Zhaoyan ^{[1
]}

Ha, Huibo ^{[1
]}

机构：

[1] Tsinghua Univ, State Key Lab Precis Measurement Technol & Instru, Dept Precis Instruments & Mechanol, Beijing 100084, Peoples R China

来源：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2006年 / 20卷 / 06期

关键词：

D O I：

10.1016/j.ymssp.2004.12.006

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 [机械工程];

摘要：

Detecting the new or anomalous signal sequences in the observed time series data is a problem of great practical interest for many applications. The bio-inspired negative selection algorithm, whose main idea is to discriminate the non-self pattern from self pattern, has drawn much attention because only normal information is needed for training. Most of the proposed algorithms are based on binary-valued string matching. A real-valued negative selection algorithm for novelty detection in vibration signal is implemented in this paper. The vector set for calculation is constructed by sampling the discrete time series from a moving time window. The matching affinity between two vectors is measured by cosine similarity. The calculated results show that the cosine similarity-based algorithm is more practical for potential applications in online signal monitoring. (c) 2005 Elsevier Ltd. All rights reserved.

引用

页码：1461 / 1472

页数：12

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