Tag: Business

Our research showed what intelligent, data-based rail head lubrication can achieve and how this understanding has developed systemically.

The next step is crucial: transferring the research results into a real operational project. A concrete example of this is the implementation at BERNMOBIL, in collaboration with Substring as technology partner.

Based on the findings from the research, a system was implemented that:

• Records the lubrication requirements on the vehicle side
• Continuously evaluates noise and operating data
• Triggers lubrication proactively and route-specifically

What is different from before is that lubrication is now demand-driven, data-based and comprehensive across the entire fleet. The principles developed in the research project were specifically refined for operational use with a focus on robustness, scalability and integration into existing processes.

The result is a practical solution that reduces noise and minimises wear while taking operational and environmental requirements into account.

Further insights into the project can be found here: https://prose.one/noise-and-wear-reduction-for-trams-with-ai-based-rail-head-lubrication/

Data-based rail head lubrication can significantly influence noise, wear and resource use. However, operators increasingly see that such solutions must be considered in the wider context of traction, braking and daily operation.

That is why we are placing the topic in a broader technical context:

On 23 April 2026 in Vienna, Dr Felix Saur, our expert in wheel-rail systems, will give a presentation at the 23rd Oberbremsrätekonferenz and the 21st International Central European Conference, an event organised by Knorr-Bremse GmbH Austria, on Intelligent traction optimisation of rail vehicles.

The conference offers another important platform for exchange between operations, technology and research, especially where friction, traction, braking and vehicle dynamics come together.

Reach out to Dr Felix Saur or Josef  Kometer to meet up. We look forward to dialogue and discussion with the expert community.

Further information on the event can be found here: https://www.oberbremsraetekonferenz.eu/cms/

Over the past two weeks, PROSE brought intelligent rail head lubrication into two key industry forums in Dresden and Vienna. We used these events to test ideas, challenge assumptions and engage with the people who deal with these challenges every day.

In Dresden, at the 21st International Railway Vehicle Conference, the focus was on intelligent rail head conditioning for trams. The combination of measurement technology, AI-supported data analysis and practical operational results sparked strong interest and many follow-up questions, both technical and operational.

In Vienna, at the 30th meeting of the Sound and Vibration Working Group (Rail Transport), the discussion shifted towards vehicle-based acoustic monitoring. The topic of acoustic data collection as the basis for tailored rail head conditioning and monitoring of rolling stock and infrastructure led to in-depth exchanges.

What stood out across both events:

There is a growing awareness that noise, wear and resource use cannot be optimised in isolation. For operators, maintainers and engineers, the key question is becoming:
How do we turn data into reliable, scalable decisions in daily operation?

These conversations build directly on the insights from our intelligent rail head lubrication research project and help translate them into practical, scalable solutions.

Our research project on intelligent rail head lubrication has shown what is technically possible when lubrication is controlled on the basis of real operational data.

Now the topic has entered the professional debate.

In the issue of ZEVrail, a detailed technical article has been published. The article presents the joint project carried out by BERNMOBIL, Substring and PROSE, funded by the Swiss Federal Office of Transport (BAV), and describes:

• The concept of demand-driven rail head lubrication
• Acoustic monitoring across 15 tram vehicles
• AI-based evaluation of audio, meta and weather data
• Predictive triggering of lubrication based on noise mapping
• The transition from static lubrication to data-driven system control

What began as a research question “How to reduce curve squeal and wear with minimal use of lubrication agents?”  has evolved into a validated, scalable system approach.

The publication shows that intelligent rail head lubrication is becoming part of the technical conversation about sustainable urban rail operations.

Read the article here (only in German): https://www.zevrail.de/artikel/die-intelligente-schienenkopfkonditionierung

Our intelligent rail head lubrication research project has shown that intelligent, data-based rail head lubrication works under real operating conditions.

However, it has also become clear that questions remain regarding the stability of the effects across different routes, vehicles, and operating conditions. Further questions concern the interaction between rail head lubrication and wheel flange lubrication, as well as how data-driven systems can be scaled without adding complexity for operators.

The focus of the project is shifting from proving feasibility to optimising and expanding the knowledge gained:

• Extending the system to additional routes and vehicle types
• Integration of intelligent flange lubrication alongside rail head lubrication
• Analysis of interactions between vehicles, infrastructure and operating conditions
• Strengthening of monitoring, control logic and operational robustness

The goal is to move from isolated applications to scalable, operational solutions that support asset maintenance, cost efficiency and environmental performance throughout the entire life cycle. The project thus marks the transition from research results to a basis for long-term application.

Further information on objectives and scope can be found here: https://www.aramis.admin.ch/Default?DocumentID=74023&Load=true

We are pleased to announce that Marco Pagani will take on the role of Co-Director of our Business Unit Switzerland as of 1 May 2026. Together with Lukas Müller, he will lead the continued development of our activities in Switzerland, working in close collaboration with customers, partners and colleagues.

Marco Pagani brings extensive experience in fleet strategy, asset management and large procurement programmes within the European railway sector. He has been responsible for international locomotive fleets and major investment projects and currently contributes to national ERTMS and high-speed train programmes in Switzerland. With technical, strategic and financial expertise, he has led international teams and delivered complex initiatives across the railway industry. This experience ensures continuity and strong leadership for the PROSE Business Unit in Switzerland.

After 11 years with PROSE, Rolf Mühlemann is moving on to pursue new opportunities. We thank him for his long-standing commitment and for the foundation he has helped build within the Business Unit.

With this transition, we remain committed to supporting our customers with independent expertise and reliable customised solutions within railway technology.

Please join us in welcoming Marco Pagani to his new role.

The results from our research project show what intelligent rail head lubrication can achieve in practice. In order to turn observed effects into robust, transferable solutions, we need a deeper understanding of the mechanisms behind wear and noise in the wheel-rail system.

For this reason, a special literature study on wear mechanisms was an integral part of the intelligent rail head lubrication research project. The study summarises the current state of scientific knowledge on the following topics:

• Rail waviness and slip waves
• Wheel polygonisation
• Friction-induced vibrations and resonances
• The role of lubrication in various operating scenarios

A key finding: Waviness and polygonal wear do not have a single cause. They arise from the interaction of vehicle, track, operation and friction. This has direct implications for operators: measures that work in one context may have unintended consequences in another.

Targeted, data-driven lubrication must therefore be combined with an understanding at the system level.

The results of the literature study help to place rail head lubrication in a broader context and form an important bridge between research results and future application and scaling.

The full literature review can be found here (only in German): https://www.aramis.admin.ch/Dokument.aspx?DocumentID=73690

The results of our research clearly show that noise, wear and tear, and resource use can only be optimised together if decisions are made on the basis of data.

Many questions are currently arising among operators, which can only be addressed through open dialogue between operators, maintainers, researchers and technologists.

That is why we want to spread the topic of intelligent rail head lubrication into various specialist formats and share our knowledge. Two events in the coming weeks will provide an important platform for this, offering different perspectives:

On 5 March 2026 in Dresden, Dr Felix Saur, our expert in wheel–rail interaction, will speak at the 21st International Railway Vehicle Conference on Self-learning rail head lubrication for trams

On 11 March 2026 in Vienna, Dr Felix Saur will provide insights at the 30th meeting of the Sound and Vibration Working Group (Rail Transport) on Intelligent, mobile lubrication systems using on-board acoustic measurement

We look forward to open discussions, critical questions and the exchange of experiences.

The results from our research are clear. The following effects were measured on a PoC track in urban operation:

• −98.5% curve squealing
• up to −60% lubrication agent consumption
• Simulations indicate a potential reduction of wheel tread wear of up to 12 %

A key learning for operators: it is not high-end hardware that is crucial, but a robust database, a sensibly defined squeal index, developed together with Substring, and an understanding of wheel-rail contact throughout the entire operation.

Intelligent lubrication reduces conflicting objectives: less noise with lower resource consumption and less wear with more stable life cycle costs. This makes intelligent rail head lubrication a building block for sustainable mobility and consistent lifecycle optimisation in urban rail transport.

These findings form the basis for exchange, further exploration and the next step towards practical application.

For those who want to get more information (only in German):

• Final report with methodology, measurements and validation: https://www.aramis.admin.ch/Default?DocumentID=73689&Load=true
• Project results presentation with key figures, system logic and learnings for operators: aramis.admin.ch/Default?DocumentID=73691&Load=true

Urban rail transport is under constant pressure. Tight curve radii, high frequencies, and increasing environmental demands lead to familiar challenges: squealing on curves, increased wheel-rail wear, and the use of lubricants with negative environmental impacts.

Conventional rail head lubrication is largely static, using fixed quantities at fixed locations, regardless of weather or actual need. This often creates a trade-off between noise, wear and environmental impact.

This is where the intelligent rail head lubrication research project comes in. Requirements are determined based on data, using:

• acoustic sensors directly on the vehicle
• continuous noise mapping along the route
• weather and operating data
• AI-supported evaluation

This means that lubrication is triggered depending on the situation and proactively, based on real operating data. This is the starting point for our research.

More background on the intelligent rail head lubrication research project, including objectives, approach and system architecture, can be found here:

– Project description: https://www.aramis.admin.ch/Default?DocumentID=73688&Load=true

– Project presentation (overview and setup, only in German): aramis.admin.ch/Default?DocumentID=73691&Load=true