An unsupervised learning approach by novel damage indices in structural health monitoring for damage localization and quantification

残余物 参数统计 自回归模型 计算机科学 可靠性(半导体) 参数化模型 系列(地层学) 模式识别(心理学) 人工智能 算法 数学 统计 生物 量子力学 物理 古生物学 功率(物理)
作者
Alireza Entezami,Hashem Shariatmadar
出处
期刊:Structural Health Monitoring-an International Journal [SAGE]
卷期号:17 (2): 325-345 被引量:122
标识
DOI:10.1177/1475921717693572
摘要

The aim of this article is to propose novel damage indices for damage localization and quantification based on time series modeling. In order to extract damage-sensitive features from time series models, it is essential to choose adequate and robust orders in such a way that the models are able to extract uncorrelated residuals. On this basis, a new iterative order determination method is proposed to select robust orders of time series models under residual analysis by Ljung–Box Q-test. The damage-sensitive features are the parameters and residuals of an AutoRegressive (AR) model obtained from current feature extraction approaches. In this study, the AR model is identified as the most compatible time series model with measured vibration time-domain responses using Box–Jenkins methodology and Leybourne–McCabe hypothesis test. The proposed damage indices are the parametric assurance criterion and the residual reliability criterion that exploit the parameters and residuals of AR models, respectively. The main idea behind locating a damage is to define threshold limits for both damage indices using the features of undamaged conditions based on an unsupervised learning way. The major contributions of this article are to propose an iterative order determination method for time series models and two novel damage indices for locating and quantifying damage. The accuracy and performance of the proposed methods are experimentally demonstrated on a three-story laboratory frame and a model-scale steel structure. Results show that the proposed iterative approach leads to uncorrelated residuals, and the proposed parametric assurance criterion and the residual reliability criterion methods are promising and efficient tools in damage detection problems under varying operational and environmental conditions.
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