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Entropy method for structural health monitoring based on statistical cause and effect analysis of acoustic emission and vibration signals

Citation

Tao, K and Zheng, W and Jiang, D, Entropy method for structural health monitoring based on statistical cause and effect analysis of acoustic emission and vibration signals, IEEE Access, 7 pp. 172515-172525. ISSN 2169-3536 (2019) [Refereed Article]


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Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1109/ACCESS.2019.2956289

Abstract

Acoustic emission (AE) and vibration signal are significant criteria of damage identification in structural health monitoring (SHM) engineering. Multi-disciplinary knowledge and synergistic parameter effects are technical challenges for damage assessment modelling. This study proposes a structural damage cause-and-effect analysis method based on parameter information entropy. Monitoring data is used to form a time-domain feature wave (TFW). The structural strength degradation factor (DF) would be used to define structural damage information entropy (SDIE) vector. The structural damage cause and effect model is developed in a probability sense. A fatigue index is adopted for damage assessment, and a causal strength index is proposed to locate the most likely damage cause. A sandstone-truss structure experiment was conducted to show that the proposed method is effective for damage evaluation and the experimental results provide strong support. This is a statistical damage identification method based on causal logic uncertainty, meaning a complicated mechanics calculation can be avoided.

Item Details

Item Type:Refereed Article
Keywords:structural health monitoring, acoustic emission, cause-and-effect analysis, parameter information entropy
Research Division:Engineering
Research Group:Communications engineering
Research Field:Signal processing
Objective Division:Construction
Objective Group:Building management and services
Objective Field:Civil building management and services
UTAS Author:Tao, K ( Kai Tao)
UTAS Author:Jiang, D (Dr Danchi Jiang)
ID Code:136511
Year Published:2019
Web of Science® Times Cited:1
Deposited By:Engineering
Deposited On:2020-01-03
Last Modified:2020-05-27
Downloads:18 View Download Statistics

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