Data Compression of Large-Scale FBG Sensor Networks for Health Monitoring Applications

Abstract

Fiber Bragg grating (FBG) sensors have attracted growing interest in road health monitoring due to their high sensitivity, accuracy, and resilience to harsh environmental conditions. Continuous monitoring is essential for identifying patterns in the collected data and FBG sensors meet this need by continuously measuring strain across multiple pavement layers at high sampling frequencies, creating an extensive, high-resolution dataset. However, such large data volumes present substantial challenges for transmission, storage, processing, and analysis in structural health monitoring (SHM). To address these challenges, this study introduces a data reduction approach grounded in probability theory. The proposed method utilizes a relative damage assessment framework to minimize the need for full data storage and processing. Instead of analyzing each strain measurement, this approach leverages the distribution of strain events to estimate potential structural changes. By focusing on cumulative strain event counts at specific threshold levels, it identifies shifts in strain distribution patterns that can be an indication of structural changes. Then, the effectiveness of this approach was validated through real-world data from an in-situ (field) monitoring campaign. This streamlined data interpretation process significantly reduces the volume of data for storage and processing, thereby enabling efficient real-time damage assessment of complex infrastructure systems. Overall, the probability-based data reduction method proposed provides a scalable and responsive solution for SHM systems utilizing FBG sensors, particularly in applications requiring dense sensor networks and continuous monitoring. This approach holds promise for enhancing the scalability and efficiency of SHM systems, especially in large-scale infrastructure projects.