In this paper, the ideas of deep auto-encoder (DAE) and manifold learning are adopted to solve the problem of structural condition diagnosis. A scalable decentralized end-to-end unsupervised structural condition diagnostic framework is proposed. Three damage diagnosis mechanisms are clarified. The structural damage diagnosis approaches are presented from the latent coding domain and the time domain, respectively. In the latent coding domain, an undercomplete DAE is established to extract the distribution of the low-dimensional manifold of the signal. On the contrary, in the time domain, an overcomplete DAE is adopted to extract the reconstruction error of the signal. Subsequently, normalized damage quantitative indicators are developed in the two domains. The damage localization method is also clarified. The proposed method can extract features directly from original vibration data without the need for additional signal preprocessing techniques. More importantly, the algorithm relies only on the output signals and does not require a numerical or scale model. This framework can be used to identify, locate, and quantify structural damages. The validity of the diagnostic framework is verified using a well-designed laboratory benchmark structure. A large-scale grandstand structure is further used to prove the ability of the proposed method for identifying slight structural damage caused by the loosening of joint bolts. The results clearly demonstrate an elegant performance of the proposed damage detection algorithm in structural condition assessment and damage localization.

中文翻译：

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使用深度自动编码器的分散式无监督结构状态诊断方法

本文采用深度自动编码器（DAE）和流形学习的思想来解决结构状态诊断问题。提出了一种可扩展的分散式端到端无监督结构状态诊断框架。阐明了三种损坏诊断机制。从潜在编码域和时域分别提出了结构损伤诊断方法。在潜在的编码域中，建立了一个不完全的DAE，以提取信号的低维流形的分布。相反，在时域中，采用了不完全的DAE提取信号的重构误差。随后，在这两个领域中开发了标准化的损害定量指标。还明确了损坏的定位方法。所提出的方法可以直接从原始振动数据中提取特征，而无需其他信号预处理技术。更重要的是，该算法仅依赖于输出信号，不需要数值或比例模型。该框架可用于识别，定位和量化结构损伤。使用精心设计的实验室基准结构可以验证诊断框架的有效性。进一步使用大型正面看台结构来证明所提出的方法能够识别由于连接螺栓的松动而引起的轻微结构损坏的能力。结果清楚地证明了所提出的损伤检测算法在结构状态评估和损伤定位中的出色表现。