A Robust Deep Learning-Based Damage Identification Approach for SHM Considering Missing Data

Abstract

Data-driven methods have shown promising results in structural health monitoring (SHM) applications. However, most of these approaches rely on the ideal dataset assumption and do not account for missing data, which can significantly impact their real-world performance. Missing data is a frequently encountered issue in time series data, which hinders standardized data mining and downstream tasks such as damage identification and condition assessment. While imputation approaches based on spatiotemporal relations among monitoring data have been proposed to handle this issue, they do not provide additional helpful information for downstream tasks. This paper proposes a robust deep learning-based method that unifies missing data imputation and damage identification tasks into a single framework. The proposed approach is based on a long short-term memory (LSTM) structured autoencoder (AE) framework, and missing data is simulated using the dropout mechanism by randomly dropping the input channels. Reconstruction errors serve as the loss function and damage indicator. The proposed method is validated using the quasi-static response (cable tension) of a cable-stayed bridge released in the 1st IPC-SHM, and results show that missing data imputation and damage identification can be effectively integrated into the proposed unified framework.

Delamination Detection Framework for the Imbalanced Dataset in Laminated Composite Using Wasserstein Generative Adversarial Network-Based Data Augmentation

“…To avoid structural failure and severe loss, it is necessary to quickly detect damage in composite structures to prolong their service life. PHM technology provides early damage detection and helps avoid the deterioration of various industrial systems [7][8][9][10][11][12]. Recently, techniques for PHM based on Machine Learning (ML) and Deep Learning (DL) using vibration signals have been continuously adopted for fault diagnosis in various structures.…”

Multi-Damage Detection in Composite Space Structures via Deep Learning

“…However, as the collected data usually consist of sequential time series signals, the most efficient deep learning architecture to be applied for prediction and classification falls within the RNNs domain [24]. In particular, it has been demonstrated that the advanced sub-class named Long Short-Term Memory (LSTM) networks [25,26] yields superior results when analysing and classifying time series without the need for complex preprocessing and hand-crafted feature extraction [27]. This paper introduces an LSTM architecture to investigate structural health monitoring for space systems, given its relatively new application to this sector.…”

“…Data normalisation: This step is crucial to ensure the data are in the proper range of the dynamic variability in the learning space of the DL network. It was proven [25] that, for this type of application, normalisation with respect to the mean and standard deviation of samples offers better results than minimum-maximum processing.…”

Research on the Identification of Bridge Structural Damage Using Variational Mode Decomposition and Convolutional Self-Attention Neural Networks

“…In recent years, the establishment of an efficient health monitoring system capable of accurately detecting internal signals from civil structures has emerged as a prominent concern in the field of civil engineering [1][2][3][4]. In the structural health monitoring system (SHMS), classical on-site measurement methods such as ambient vibration testing [5], forced vibration testing [6], and impact vibration testing [7], which are direct methods, often require the installation of numerous sensors directly on the bridge structure [8,9], and the selection of a vibration parameter that is sensitive to structural damage (e.g., frequency [10], mode shape [11], strain energy [12], etc.…”