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Applying robust variant of Principal Component Analysis as a damage detector in the presence of outliers

机译：在存在异常值的情况下将主成分分析的强大变体用作损坏检测器

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摘要

Using Principal Component Analysis (PCA) for Structural Health Monitoring (SHM) has received considerable attention over the past few years. PCA has been used not only as a direct method to identify, classify and localize damages but also as a significant primary step for other methods. Despite several positive specifications that PCA conveys, it is very sensitive to outliers. Outliers are anomalous observations that can affect the variance and the covariance as vital parts of PCA method. Therefore, the results based on PCA in the presence of outliers are not fully satisfactory. As a main contribution, this work suggests the use of robust variant of PCA not sensitive to outliers, as an effective way to deal with this problem in SHM field. In addition, the robust PCA is compared with the classical PCA in the sense of detecting probable damages. The comparison between the results shows that robust PCA can distinguish the damages much better than using classical one, and even in many cases allows the detection where classic PCA is not able to discern between damaged and non-damaged structures. Moreover, different types of robust PCA are compared with each other as well as with classical counterpart in the term of damage detection. All the results are obtained through experiments with an aircraft turbine blade using piezoelectric transducers as sensors and actuators and adding simulated damages.
机译：在过去的几年中，使用主成分分析（PCA）进行结构健康监测（SHM）受到了广泛的关注。 PCA不仅被用作识别，分类和定位损害的直接方法，而且还被用作其他方法的重要第一步。尽管PCA传达了几个积极的规范，但它对异常值非常敏感。离群值是异常观察结果，可能会影响方差和协方差，这是PCA方法的重要组成部分。因此，在存在异常值的情况下基于PCA的结果不能完全令人满意。作为主要贡献，这项工作建议使用对异常值不敏感的PCA的强大变体，作为解决SHM领域中此问题的有效方法。此外，在检测可能的损坏方面，将健壮的PCA与经典PCA进行了比较。结果之间的比较表明，健壮的PCA可以比使用经典的PCA更好地区分损坏，即使在许多情况下，也可以在经典PCA无法区分损坏和未损坏的结构的情况下进行检测。此外，就损伤检测而言，将不同类型的鲁棒PCA以及与经典PCA进行了比较。所有结果都是通过使用压电传感器作为传感器和执行器并增加模拟损伤的飞机涡轮叶片进行实验获得的。

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  • 来源
    《Mechanical systems and signal processing》 |2015年第1期|467-479|共13页
  • 作者

    Fahit Gharibnezhad; Luis E. Mujica; Jose Rodellar;

  • 作者单位

    Control, Dynamic and Applications Group (CoDAlab), Department of Applied Mathematics Ⅲ, Escola Universitaria d'Enginyeria Tecnica Industrial de Barcelona (EUETIB), Universitat Politecnica de Catalunya (UPC), Comte Urgell 187, 08036 Barcelona, Spain;

    Control, Dynamic and Applications Group (CoDAlab), Department of Applied Mathematics Ⅲ, Escola Universitaria d'Enginyeria Tecnica Industrial de Barcelona (EUETIB), Universitat Politecnica de Catalunya (UPC), Comte Urgell 187, 08036 Barcelona, Spain;

    Control, Dynamic and Applications Group (CoDAlab), Department of Applied Mathematics Ⅲ, Escola Universitaria d'Enginyeria Tecnica Industrial de Barcelona (EUETIB), Universitat Politecnica de Catalunya (UPC), Comte Urgell 187, 08036 Barcelona, Spain;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Damage detection; Robust PCA; Outliers;

    机译：损坏检测;强大的PCA;离群值;

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