A comprehensive method for sensor fault detection and signal reconstruction in structural health monitoring systems for super-tall buildings

Lavassani, Seyed Hossein Hosseini; Doroudi, Rouzbeh; Shahrouzi, Mohsen; Hejazi, Farzad

Abstract

Structural Health Monitoring (SHM) systems are crucial for ensuring the integrity and safety of large-scale infrastructures. However, they are often compromised by sensor faults, which can result in false alarms or data gaps. In this study, a new comprehensive approach is developed that combines Deep Signal Anomaly Detection (DSAD), One-Dimensional Convolutional Neural Networks (1D-CNNs), and Support Vector Regression (SVR) to effectively detect, classify, and reconstruct faulty sensor data in SHM systems. Using acceleration responses from the Milad Tower in Iran as a case study, the approach begins with DSAD for anomaly detection, where the threshold is determined by an Isolation Forest. The subsequent classification of fault types is performed using 1D-CNNs, optimized through Observer-Teacher-Learner-Based Optimization (OTLBO) for enhanced accuracy. Feature extraction is achieved using Wavelet Transform (WT) and Multivariate Empirical Mode Decomposition (MEMD), which facilitate a robust representation of time and time-frequency features. Faulty signals are reconstructed using SVR, with feature selection and reduction managed through Multi-Objective Observer-Teacher-Learner-Based Optimization (MOOTLBO) to minimize reconstruction errors. The results demonstrate a high fault detection accuracy of 94.04 % and effective signal reconstruction with optimized feature input, highlighting the potential of this approach to improve the reliability of SHM systems in super-tall structures. This study effectively identifies faulty signals, classifies fault types, and reconstructs them, addressing issues related to missing and faulty signals.

• Journal Article Type: Article
• Acceptance Date: Apr 2, 2025
• Online Publication Date: Apr 11, 2025
• Publication Date: May 31, 2025
• Deposit Date: Apr 15, 2025
• Publicly Available Date: Apr 15, 2025
• Journal: Structures
• Electronic ISSN: 2352-0124
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 75
• Article Number: 108866
• DOI: https://doi.org/10.1016/j.istruc.2025.108866
• Public URL: https://uwe-repository.worktribe.com/output/14304982
• Additional Information: This article is maintained by: Elsevier; Article Title: A comprehensive method for sensor fault detection and signal reconstruction in structural health monitoring systems for super-tall buildings; Journal Title: Structures; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.istruc.2025.108866; Content Type: article; Copyright: © 2025 The Author(s). Published by Elsevier Ltd on behalf of Institution of Structural Engineers.