残余物
自编码
主成分分析
离群值
异常检测
人工智能
计算机科学
深度学习
模式识别(心理学)
数据挖掘
机器学习
算法
作者
Ana Fernández-Navamuel,Filipe Magalhães,Diego Zamora-Sánchez,Ángel Javier Omella,David García-Sánchez,David Pardo
标识
DOI:10.1177/14759217211041684
摘要
This paper proposes a Deep Learning Enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. We employ partially explainable autoencoder architecture to replicate and enhance the data compression and reconstruction ability of PCA. The particularity of the method lies in the addition of residual connections to account for nonlinearities. We apply the proposed method to monitoring data obtained from two bridges under real operation conditions and compare the results before and after adding the residual connections. Results show that the addition of residual connections enhances the outlier detection ability of the network, allowing to detect lighter damages.
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