SONOBAL Project
ROVER RAIL in collaboration with the Polytechnic University of Valencia, develops the SONOBAL system, a completely new railway auscultation technology for detecting the degree of ballast contamination by analyzing and processing acoustic signals.
The main objective of the project is the implementation of an advanced geophysical survey system of railway superstructure using non-invasive techniques based on machine learning analysis of acoustic signals. Inspired by sonar technology, the SONOBAL system assembled in a railway maintenance machine is based on the emission and reception of directional acoustic signals on the ballast, taking advantage of the absorption properties of the material itself. The sound waves are heightened when the ballast is dirty, that is, when there is fine particles between the gravels, which allows to detect the contamination of the ballast for maintenance work.
The ballast bench absorbs sound because it acts as a porous material given its discrete gravel arrangement. When these signals formed by acoustic waves propagate both through the interconnected pores and small spaces of the ballast, sound energy is dissipated through friction with their walls. This allows its degree of rigidity to be correlated with the level of ballast contamination.
Ballast pollution is a complex process of vital importance for the maintenance and conservation of railways. Affects the vertical stiffness of the railway platform and impairs the dynamic behaviour of the superstructure to dampen the passage of trains. Is caused by multiple endogenous and exogenous factors such as:
– The circulation of trains and heavy track machinery.
– Rupture and abrasion of the cracks due to friction between them.
– Fine contamination of materials that filter through the trace.
This research project responds to the current needs of the main managers of railway infrastructure maintenance and upkeep. This means that it will make available to ROVER RAIL customers an innovative technology that allows the degree of ballast contamination to be obtained automatically, continuously and non-invasively, minimizing acquisition / operation costs compared to other technologies available on the market.
Thanks to the implementation of this system, it will be possible to detect anomalies in the compactness of the ballast, its degree of contamination and pathologies associated with the platform that may be found due to the variation in track stiffness. Decision-making for railway maintenance will be optimized. The SONOBAL technology makes it possible to determine the actions required for process improvement, thanks to being a quasi-real time analysis management system throughout the life cycle of the superstructure. This technology will minimize the safety risks associated with the dynamic behaviour of the subplatform to absorb and dampen the vertical pressure exerted by the passage of trains.
This research project is funded by the State Research Agency of the Ministry of Science, Innovation and Universities through NextGenerationEU funds Ref. CPP2023-010518.
