Structural health monitoring (SHM) helps prevent unexpected failures, supports predictive maintenance, and ensures long-term safety and reliability of critical infrastructure. Real-life examples include monitoring fatigue in wind turbine blades, detecting corrosion in oil and gas pipelines, ensuring the safety of railway tracks, and maintaining the integrity of aerospace components.

Distributed fiber optic sensing (DFOS) represents a cutting-edge technology for SHM, providing a highly sensitive and comprehensive method to detect and localize defects in large structures. By integrating fiber optic sensors, this cost-effective approach enables continuous, real-time assessment of structural integrity, offering significant advantages over traditional point-based sensors.

Professor Aldo Minardo and his team at the University of Campania “Luigi Vanvitelli" use data acquisition boards from Teledyne SP Devices in their DFOS system to detect cracks and assess damage severity, leveraging AI techniques such as machine learning and neural networks to interpret complex signal responses.

The system performs damage classification and localization, predicting not only whether damage is present but also how severe it is and where it is located. Compared to other non-destructive techniques (NDTs) adopted in the SHM of civil and aerospace structures, such as radiographic testing or eddy current testing, the proposed approach can be applied in real time over the structure under operating conditions. Furthermore, the optical fiber sensor can be easily installed on the monitored structure compared to multiple PZTs or strain gauges.​