Monitoring rail condition – an objective basis for prioritized maintenance
Rail infrastructure spans large networks with very different loads, operating conditions, and ages. Maintenance decisions are therefore based less on the absolute condition of individual measurement points and more on the comparability of different sections of track.
Only through continuous recording of the rail condition along the network can differences, developments, and focal points be made visible. Conspicuous sections (“hotspots”) can thus be classified in the context of the entire network – independently of individual observations or isolated inspections.
A comparable representation of the condition forms the basis for prioritizing maintenance measures across the network and deploying resources where they achieve the greatest technical and economic effect. On this basis, damage developments can be identified at an early stage and major damage and noise-related complaints from residents can be avoided.
SERVICE PORTFOLIO
Monitoring rail condition with CMS.Rail
- Continuous recording of rail condition data along the network
- Identification of track sections with conspicuous or critical conditions
- Preparation of condition information for network-wide comparability
- Provision of a reliable basis for decision-making for prioritized maintenance measures
- Early detection of damage developments
The advantages at a glance
Objective assessment of
rail condition across the network
Efficient use
of resources
Early identification
of hotspots
Prevention of noise-related complaints
from residents and passengers
Prioritized planning
of maintenance measures
Please contact us personally
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Would you like to learn more about CMS.Rail? We would be happy to present the software to you in a personal appointment and answer your questions.
Nanostructure Technology (today's name: Quantum Engineering) at University of Würzburg
M. Sc.
Competence Network AI in SMEs "AI-HUB Northern Bavaria"
Real innovation requires not only knowledge, experience and creativity, but also a strong team and communication.
Because Wölfel is active across industries in the areas of expertise of vibrations, structural dynamics and acoustics, new challenges, customer requirements and knowledge transfers are constantly arising. This always opens up new use cases for the application of smart data-driven algorithms for us and our customers.
Nanostructure Technology (today's name: Quantum Engineering) at University of Würzburg
Competence Network AI in SMEs "AI-HUB Northern Bavaria"
Real innovation requires not only knowledge, experience and creativity, but also a strong team and communication.
Because Wölfel is active across industries in the areas of expertise of vibrations, structural dynamics and acoustics, new challenges, customer requirements and knowledge transfers are constantly arising. This always opens up new use cases for the application of smart data-driven algorithms for us and our customers.
Nanostructure Technology (today's name: Quantum Engineering) at University of Würzburg
M. Sc.
Competence Network AI in SMEs "AI-HUB Northern Bavaria"
Real innovation requires not only knowledge, experience and creativity, but also a strong team and communication.
Because Wölfel is active across industries in the areas of expertise of vibrations, structural dynamics and acoustics, new challenges, customer requirements and knowledge transfers are constantly arising. This always opens up new use cases for the application of smart data-driven algorithms for us and our customers.
Nanostructure Technology (today's name: Quantum Engineering) at University of Würzburg
Competence Network AI in SMEs "AI-HUB Northern Bavaria"
Real innovation requires not only knowledge, experience and creativity, but also a strong team and communication.
Because Wölfel is active across industries in the areas of expertise of vibrations, structural dynamics and acoustics, new challenges, customer requirements and knowledge transfers are constantly arising. This always opens up new use cases for the application of smart data-driven algorithms for us and our customers.