Integration of predictive measurement for railway infrastructure

HBK demonstrates modular predictive measurement systems at InnoTrans 2026, held from September 22 to 25 in Berlin, Germany, to protect overloaded railway infrastructure. The technical collaboration integrates wayside sensors, optical systems, and artificial intelligence to optimize fleet availability and track maintenance.

Operational Challenges and Collaboration
Global rail networks face structural challenges as increasing traffic and load volumes accelerate mechanical wear, where minor increases in vehicle overload damage tracks with the exponential power of four. To address escalating maintenance costs, premature wear, and emergency repair budgets, rail operators collaborate with HBK. This cooperation integrates physical monitoring sensors with advanced analytics, shifting infrastructure management from reactive repairs to targeted predictive upkeep to maximize fleet availability.

Technical Solution and Responsibilities
The technical architecture is based on a portfolio of metrology-grade monitoring systems designed for rapid trackside installation. HBK provides ARGOS wayside systems, which execute automated train diagnostics including weigh-in-motion, wheel impact load detection, out-of-roundness measurements, and running behavior tracking. The infrastructure incorporates NewLight extension fiber-optic sensors based on fiber Bragg grating technology to extend monitoring range, alongside pantograph monitoring solutions for live optical safety tracking of the train-to-overhead line interface. Furthermore, the architecture integrates ReliaSoft and HBK Monitor 360 software platforms to process real-time data and enable reliability engineering.

Deployment and Implementation
The physical deployment of these modular monitoring systems occurs on operational tracks, overhead lines, and within tunnels and bridges. During the implementation phase, high-accuracy sensors and precision inclinometers are installed directly trackside to monitor structural alignment and railway path stability. The technologies and system integration are presented at the InnoTrans trade fair, taking place from September 22 to 25, 2026, in Berlin, Germany, at Hall 23, Booth 315.

Applications and Use Cases
This predictive measurement technology is applied directly to railway track maintenance, structural health monitoring, and rolling stock diagnostics. Functional use cases involve executing continuous monitoring to detect rolling stock defects before they cause physical delays or catastrophic wire tear-downs. Applying this analytical architecture stabilizes network availability and allows maintenance teams to schedule planned tune-ups rather than performing high-cost emergency infrastructure repairs.

Results and Expected Impact
The integration of wayside sensors and predictive software stabilizes the structural integrity of overloaded rail networks and extends the lifespan of critical assets. Continuous data streams prevent undetected vehicle faults from causing progressive network damage. Regarding the system's operational capability, Dietmar Maicz, Country Manager at HBK, explains: "Increasing traffic density means that even minor, undetected vehicle faults can severely compromise track infrastructure almost overnight. With the right combination of high-accuracy sensors and intelligent analytics, we can help them dramatically extend the operational life of both their fleets and their tracks."

Edited by Maria Brueva, Induportals editor – adapted by AI.

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