A decentralized unsupervised structural condition diagnosis approach using deep auto‐encoders

Abstract 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.