Improving autoencoder-based unsupervised damage detection in uncontrolled structural health monitoring under noisy conditions
Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.
structural health monitoringguided wavesprincipal component analysisdeep learningdenoisingdynamic environmental condition
杨抗、王淋元、高超、陈默之、周敦之、刘洋
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佛罗里达大学电子信息与计算机工程学院 盖恩斯维尔32611
天普大学电子信息与计算机工程学院费城 19122
天津大学精密仪器与光电子工程学院 天津 300072
结构健康监测 导波 主成分分析 深度学习 降噪 动态环境
National Science Foundation of Zhejiang under Contract
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