FIBER-OPTIC HEALTH AND USAGE MONITORING SYSTEM: ADVANCED MATERIALS, PHOTONIC SENSING AND PREDICTIVE ALGORITHMS

The PhD positions are developed within the framework of a 5-years project, which aims at advancing predictive diagnostics and smart maintenance strategies for railway infrastructure through the adoption of photonic sensing technologies, with particular emphasis on fiber optic sensors.

Railway maintenance is still largely based on scheduled inspections and manual measurements carried out directly on the infrastructure. Although this approach is well established, it is often costly, time-consuming, and only partially representative of the actual structural and operational condition of critical components. Moreover, on-site inspection activities typically require traffic interruptions and expose operators to potentially hazardous environments. In this context, the development of continuous, remote, and reliable monitoring solutions is a key step toward the transition from conventional planned maintenance to predictive maintenance.

Among the available sensing technologies, fiber optic sensors are particularly promising for railway applications due to their low invasiveness, durability, multiplexing capability, and immunity to electromagnetic interference. Nevertheless, their large-scale adoption in the railway sector is still limited by several unresolved issues, including fragility of the optical fiber, complexity of installation, difficulty of integration into real components, and the need for sensing modules specifically tailored to harsh operating environments.

The main objective of this research is therefore the design, numerical modelling, fabrication, and experimental validation of innovative fiber optic based sensors for the monitoring of critical railway infrastructure elements. The study will focus on sensing solutions for selected railway components, such as insulated joints, switches, continuous welded rails, and overhead contact line elements, with the aim of identifying robust and easily deployable architectures capable of providing meaningful information for infrastructure diagnostics.

From a methodological point of view, the research will begin with numerical simulations (Abaqus) aimed at analysing the mechanical behaviour of different sensing configurations and the interaction between the monitored component, the host structure, and the embedded or integrated optical fiber. Attention will be devoted to strain transfer mechanisms, sensor sensitivity, structural reliability, packaging solutions, and the optimization of the sensing element geometry. These activities will provide the basis for a rational design process, supporting the definition of sensor layouts capable of maximizing measurement performance while ensuring adequate protection of the optical fiber.

A second major research task concerns the manufacturing and integration of the sensing element. This phase will address the technological challenges associated with fiber embedding or bonding, connector integration, material selection, and the development of protective and functional host structures. Since practical deployability is a central requirement for railway applications, the research will also investigate installation strategies compatible with the operational and environmental constraints of the infrastructure.

The final phase will consist of the experimental characterization and validation of the developed solutions through laboratory testing. The sensors will be assessed in terms of response, repeatability, robustness, and consistency with numerical predictions. The comparison between simulations and experiments will make it possible to refine the designs and evaluate the technological potential of the proposed solutions for future implementation in predictive diagnostic systems.

The expected outcome of the PhD is a methodological and technological contribution to the development of integrated photonic sensing solutions for railway infrastructure monitoring, helping bridge the gap between laboratory-scale fiber optic sensing technologies and their effective adoption in real-world railway maintenance scenarios. In a broader perspective, the research may also support the future development of data-driven maintenance procedures, digital twins, and advanced structural health monitoring frameworks for critical infrastructure.

Funding Notes

Project funded by Italian Ministry of Research

References

1. G. Sala, L. Di Landro, A. Airoldi, P. Bettini (2015). Fibre optics health monitoring for aeronautical applications. MECCANICA, Vol. 50, N. 10, p. 2547-2567, Doi: 10.1007/s11012-015-0200-6
2. D. Rigamonti, P. Bettini (2023). Enabling FO-Based HUMS Applications Through an Innovative Integration Technique: Application to a Rotor Blade Mockup. LECTURE NOTES IN CIVIL ENGINEERING, 2023, 270 LNCE, pp. 921–932
3. P. Bettini, G. Sala (2007). Preliminary Assessment of Helicopter Rotor Blades Fatigue Endurance Through Embedded F.O.Sensors. In: 24th International Committee on Aeronautical Fatigue (ICAF) Symposium. Napoli, Italy, 16-18 May 2007, p. 1-12
4. P. Bettini, S. Bertoli, G. Sala, R. Gaspari, G. Pozzati (2012). Development of State-of-the-Art Optical Sensors for the Monitoring of Deep Sea Umbilicals and Flexible Pipelines. In: Industrial and Commercial Applications of Smart Structures Technologies, Kevin Farinholt, Steven F. Griffin (Eds.), SPIE, Washington, 2012, ISBN: 9780819490001, p. 1-11
5. A. Airoldi, P. Ballarin, S. Di Mauro, D. Rigamonti, F. Reinert, M.M. Dadras, S. Zabihzadeh, E. De Nicolò, P. Bettini, L. Cartabia (2023). Development of an Additive Manufactured Fitting Sensorized with Optical Fibres for Load Recognition. In Proceedings of AIAA Scitech 2023 Forum, AIAA, 2023, ISBN: 9781624106996, p. 1-14, National Harbor, MD, USA & Online, 23-27 Jan. 2023] DOI: 10.2514/6.2023-2079
6. E. Casciaro, D. Rigamonti, P. Bettini (2024). Birefringent optical fibers to decouple thermo-mechanical effects on FBG sensors. In: Proceedings of 11th European Workshop on Structural Health Monitoring EWSHM2024, Potsdam (Germany),10-14 June, (indexed SCOPUS)