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首页> 外文期刊>Journal of the Brazilian Society of Mechanical Sciences and Engineering >Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition
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Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

机译：使用AR-ARX模型和统计模式识别的基准结构中的损坏检测

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Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure.
机译：结构健康监视（SHM）与监视状态并确定航空，民用和机械系统内损坏或退化程度的能力有关。从这个意义上讲，本文研究了两步自回归和外源自回归（AR-ARX）模型在结构系统损伤诊断的线性预测中的应用。此损坏检测算法基于通过振动响应测量获得的残余误差作为损坏敏感指标的监视。在复杂的结构中，要观察的位置很多，并且要处理大量数据，这使得信号的可视化变得困难。本文还研究了使用主成分分析的数据压缩。为了建立阈值，采用模糊c均值聚类在无监督学习模式下量化损伤敏感指标。按照IASC-ASCE提出的基准测试问题进行测试，并采用不同的损坏方式。获得的诊断结果与结构的实际完整性状态高度相关。

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