Research on stress curve clustering algorithm of Fiber Bragg grating sensor

The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on...

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Bibliographic Details
Published in:Scientific reports Vol. 13; no. 1; pp. 11815 - 12
Main Authors: Lin, Yisen, Wang, Ye, Qu, Huichen, Xiong, Yiwen
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 21.07.2023
Nature Publishing Group
Nature Portfolio
Subjects:
639/166/987
639/705/117
Algorithms
Classification
Clustering
Humanities and Social Sciences
multidisciplinary
Science
Science (multidisciplinary)
Sensors
Stress
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on density clustering algorithm is proposed. To solve the problem of large dimension and sparse sample space of sensor stress curve, the distance between samples is measured based on improved cosine similarity. Aiming at the problem of low efficiency and poor effect of traditional clustering algorithm, density clustering algorithm based on mutual nearest neighbor is used to cluster. Finally, the classification of the daily stress load characteristics of the sensor is realized, which provides a basis for constructing the mathematical analysis model of building health. The experimental results show that the stress curve clustering method proposed in this paper is better than the latest clustering algorithms such as HDBSCAN, CBKM, K-mean++,FINCH and NPIR, and is suitable for the feature classification of stress curves of fiber Bragg grating sensors.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-39058-w