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Damage detection using enhanced multivariate statistical process control technique

机译：使用增强的多元统计过程控制技术进行损坏检测

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摘要

This paper addresses the problem of damage detection technique of structural health monitoring (SHM). Kernel principal components analysis (KPCA)-based generalized likelihood ratio (GLR) technique is developed to enhance the damage detection of SHM processes. The data are collected from the complex three degree of freedom spring-mass-dashpot system in order to calculate the KPCA model. The developed KPCA-based GLR is the method that attempts to combine the advantages of GLR statistic in the cases where process models are not available and a multivariate statistical process control; KPCA. The simulations show the improved performance of the KPCA-based GLR damage detection method.
机译：本文解决了结构健康监测（SHM）的损伤检测技术问题。开发了基于核主成分分析（KPCA）的广义似然比（GLR）技术，以增强对SHM流程的损伤检测。为了计算KPCA模型，从复杂的三自由度弹簧-质量-阻尼器系统中收集了数据。基于KPCA的已开发GLR是一种试图在没有可用的过程模型和多变量统计过程控制的情况下结合GLR统计的优点的方法; KPCA。仿真显示了基于KPCA的GLR损坏检测方法的改进性能。

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