Predictive Maintenance in rail transportation

Smooth and uninterrupted railway operations rely heavily on the maintenance of rail vehicles, components, and infrastructure. Traditionally, a cyclic maintenance approach has been employed. However, this approach poses challenges. If maintenance intervals are too short, it results in excessive maintenance costs, while longer intervals can lead to costly disruptions and failures.

Optimized maintenance intervals enable uninterrupted operations with lower maintenance costs

Determining the need for maintenance and upkeep measures is not solely dependent on elapsed time. Numerous factors, such as

utilization and load,
environmental and operational influences
age of components

influence the wear and tear of rail vehicles, individual components, or infrastructure. These factors are typically measurable. Our core competency lies in monitoring, with a focus on vibration and acoustic data in conjunction with operational data and integrating them into monitoring systems. For this purpose, algorithms are developed based on mechanical models, enhanced by AI components. The objective is to identify precise condition indicators and key performance indicators (KPIs). With these insights, infrastructure and components can be continuously monitored and maintained based on their condition.

[Translate to English:] Rail Anomaly Indicator 2019

OFFER & CONSULTING

Predictive Maintenance is a broad term that has been discussed in many industries for quite some time. The benefits of this concept for the rail sector are evident and widely acknowledged:

Manufacturers and operators
of rail infrastructure and
infrastructure components

Optimized monitoring of wear condition
Improved maintenance planning and management
More efficient utilization of existing personnel resources
Optimized material usage

Rail
vehicle
operators

Enhanced safety and reliability in train operations
Reduced delays and cancellations
High fleet availability and reduced need for replacement vehicles

Manufacturers
of rail vehicles
and vehicle components

Identification of weak spots to optimize future products
Improved vehicle availability and reliability
Increased customer satisfaction

Achieving predictive maintenance in three steps: your customized approach

For many, the real challenge lies in implementation. What data should we gather? And, more importantly, how can we extract concrete value from this data?

Our three-step approach makes integrating predictive maintenance into your existing system far more straightforward than you might anticipate. Explore each step by hovering your cursor over it to gain deeper insights:

Our service portfolio in the field of maintenance and repair in rail transportation

Optimization of maintenance intervals
Monitoring of critical infrastructure components (rail, switch, track alignment)
Evaluation of operational stress factors (shock monitoring, dynamic loads)
Monitoring of maintenance-relevant components (wheelset, bogie, wheel bearings)
Early detection of defects and anomalies
Load detection (service life, validation, design loads)

WHAT MAKES US EXPERTS?

Multiple units, freight wagons, tracks, or switches: Each of them reveals valuable indicators about their condition - not only during on-site maintenance but also remotely. Over many years, we have acquired the necessary expertise to decipher these signs.

The starting point is always capturing the right data. For more than 50 years, measuring and monitoring vibrations and acoustic signals have been integral parts of our service portfolio, offered both as services and through product solutions. Our strength lies in combining data from different sources to generate added value.

Comprehensive knowledge in signal analysis is essential for reliable diagnostics and predictions based on captured data. Starting with the monitoring and analysis of simple to complex structures in mechanical engineering and wind energy, we have gathered extensive experience. We have successfully transferred this expertise to various industries, including the railway sector. For several years now, we have been consistently integrating AI components into our analysis concepts, enabling us to extract even more value from the data.

The proper server infrastructure is crucial for processing and managing signals in compliance with data protection regulations. We are well-equipped in this area, with a broad and up-to-date knowledge of the latest technologies. We customize the server infrastructure according to your specific requirements and offer the option to fully or partially take over its management.

Thanks to our extensive experience, we are capable of identifying untapped potential. Often, the key lies not in completely restructuring entire processes but rather in intelligently utilizing existing data assets.

Offer & Consulting

Please contact us personally

________

We will be happy to advise you on multibody simulations and assist you in developing, optimizing, and automating your processes.

Dr.-Ing. Manuel Eckstein
Head of Simulation + Predictive Maintenance

+49 931 49708-290
eckstein@woelfel.de
Contact form

Study

Mechanical Engineering, TU Darmstadt

Acadamic Degree

Dr.-Ing.

Working at Wölfel

Mastering the variety of technical challenges together with our customers and with the Wölfel team through innovative solutions.

M. Sc. Bernhard Huber
AI-Specialist

+49 931 49708-295
huber@woelfel.de
Contact form

Study

Nanostructure Technology (today's name: Quantum Engineering) at University of Würzburg

Acadamic Degree

M.Sc.

Committee Activity

Competence Network AI in SMEs "AI-HUB Northern Bavaria"

Professional Motto

Real innovation requires not only knowledge, experience and creativity, but also a strong team and communication.

Working at Wölfel

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.

Study

Mechanical Engineering, TU Darmstadt

Acadamic Degree

Dr.-Ing.

Working at Wölfel

Mastering the variety of technical challenges together with our customers and with the Wölfel team through innovative solutions.

Study

Nanostructure Technology (today's name: Quantum Engineering) at University of Würzburg

Acadamic Degree

M.Sc.

Committee Activity

Competence Network AI in SMEs "AI-HUB Northern Bavaria"

Professional Motto

Real innovation requires not only knowledge, experience and creativity, but also a strong team and communication.

Working at Wölfel