In most real‐world monitoring scenarios, the lack of measurements from damaged conditions requires the application of unsupervised approaches, mainly the ones based on modal features estimated from raw vibration data through traditional system identification methods. Although numerous successful applications using modal parameters have been reported, they have demonstrated to be insufficient to estimate a robust set of damage‐sensitive features. Inspired by the idea of compressed sensing and deep learning, an intelligent two‐level feature extraction approach using stacked autoencoders over pre‐processed vibration data is proposed. This procedure can improve the performance of traditional damage detection classifiers by compressing modal parameters into a smaller set of highly informative features when considering information entropy metrics. The proposed technique demonstrates significant improvement in the performance of damage detection and classification approaches when evaluated on the well‐known monitoring data sets from the Z‐24 Bridge, where several damage scenarios were carried out under rigorous operational and environmental effects.

中文翻译：

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使用堆叠式自动编码器的损伤敏感特征提取，可进行无监督的损伤检测

在大多数现实世界的监视场景中，由于缺少对损坏条件的测量，因此需要应用无监督方法，主要是基于通过传统系统识别方法从原始振动数据估算出的模态特征的方法。尽管已经报道了使用模态参数的许多成功应用，但已证明它们不足以估计一组可靠的损伤敏感功能。受压缩感知和深度学习思想的启发，提出了一种使用堆叠式自动编码器对预处理后的振动数据进行智能化的两级特征提取方法。当考虑信息熵度量时，此过程可以通过将模态参数压缩到较小的一组高信息量特征中，从而提高传统损伤检测分类器的性能。