Enhanced sensor fault detection in bridge monitoring using Siamese-based encoder-decoder

Bridge monitoring is crucial for ensuring the safety of these infrastructures, as they are constantly exposed to environmental stress, aging, harsh weather conditions, and intense traffic loads. Traditional inspection methods are often labor-intensive and prone to human errors, motivating the development of automated and data-driven approaches based on sensor networks directly mounted on the bridges. For this reason, a key challenge in such monitoring systems lies in the accurate detection of sensor faults, which can severely compromise the quality of collected data and, consequently, the reliability of the monitoring outcomes. This study presents a novel data-driven approach for sensor fault detection in bridge monitoring applications, based on a combination of an encoder-decoder architecture and a Siamese network. The former aims to reconstruct the spectrograms computed from the accelerometer signals, whereas the latter aims to learn a similarity metric to better discriminate faulty readings from healthy ones. The experimental results of this study demonstrate the potential of the proposed approach in enhancing sensor fault detection performance compared to several baselines, providing more accurate predictions even with small fault intensities.