Rolling equipment data and maintenance

Rolling mill data and maintenance – how to get the most out of the set

Digitization is making its way into railroad maintenance and railway infrastructure. Amid all the hype, it’s worth remembering that data collection is only one part, the key thing is to understand how to use it.

Finnish railway maintenance company VR FleetCare Invest in using data analytics for several years. The company has an in-house data analysis team and data processing platform.

Rolling equipment data and maintenance

The data can and should be used in railroad maintenance

© VR FleetCare

VR FleetCare data scientists Curtis Wood and Otto Sormonen said:

“In practice, we collect data using a variety of IoT gauges, analyze the data and use it to develop maintenance work on rail equipment.”

The data can be used to obtain operational effects, such as improvement of maintenance intervals and changes related to actual operation. As an example, Wood cites the meter in the road to the entrance to the Helsinki warehouse, which VR FleetCare It began using, among other types of data collection, to measure the wheel profiles of passing InterCity trains.

Wood explains the results:

“The life cycle of the wheel assembly is about 10 years, and they are replenished at regular intervals.

“Between turning, the wheel profile wears, which affects the safety and usability of rolling stock, among other things. Especially if the wear is uneven.

“We collected millions of data lines for analysis during the two-year experiment. Based on the data, we developed the most suitable wheel profile for InterCity trains running on the Finnish railway network. We were also able to determine the optimal reconfiguration interval.”

Wood emphasizes:

“The new wheel shape has made it possible to significantly improve the safety and reliability of rolling stock, which will result in significant cost savings for rail car owners and operators.

“It’s easier to make decisions about process changes when you have data to back you up.”

From reactive towards proactive measures

In 2019, VR FleetCare made a big leap in trolley maintenance when, along with EKEEelectronics, it introduced a service that predicted trolley maintenance needs. The technical solution was innovative even on a global scale.

Forecasting is based on data collected and analyzed. For example, in an ideal world, defects related to an automobile’s bodywork could be identified months in advance. It is easy to understand their impact on costs and smooth traffic on time.

In a perfect world, camper failure could be expected months in advance.

The data can be used in a variety of maintenance work. VR FleetCare’s suite of SmartCare services includes a rail scanner to inspect large volumes of rail cars and digital services to monitor the status of track circuits, among other services.

Sormonin says:

“In addition to IoT equipment, data can also be collected in a representative way. For example, we carried out a project in which mechanics measured brake pad wear. Based on the data, we can identify simulated wear and tear models and help experts determine the correct maintenance intervals.”

VR FleetCare uses modern platforms and systems – the full package from data collection and integration to computation, analysis and visualization.

Sormonin says:

“We have the ability to integrate different data sources into the AWS database in the cloud, which makes it possible to collect all data related to the rolling stock in one system. This allows us to consolidate and enrich the data, facilitating more comprehensive analytics.”

Wood adds:

“In addition, we can generate automatic reports and alerts of perceived deviations from the system.”

Data and industry expertise provides a competitive advantage

Data analysis is often outsourced to a consulting firm that understands numbers, but their professional skills lack experience in the client sector. An in-house data team is an absolute competitive asset for VR FleetCare as data scientists are familiar with the rail industry and can therefore deliver higher quality and more diverse analytics, particularly to support development work.

wood stresses:

“When a railroad engineer and a data scientist sit practically at the same table, the exchange of data and collaboration is seamless. I find it also important to see the processes in the warehouse. It helps to understand the whole as well as the important details.”

Both data scientists emphasize the important role of data in the future of rail traffic maintenance. The full potential of the data has not yet been realized.

The data can and should be used in railroad maintenance.

Wood says:

“Early the cases were relatively easy, but we are constantly getting into deeper and more complex cases. The kind that couldn’t even be done without advanced algorithms like machine learning. It would create entirely new dimensions of maintenance in rail traffic.”

Sormonin says:

“The development of IoT technology has made data usage increasingly cost-effective as well.”

The main lesson from investing in data analytics was that data can and should be used for railroad maintenance. However, digitization does not replace expertise – quite the contrary. With the help of data, VR FleetCare professionals have developed rolling stock maintenance programs, improving predictability of maintenance needs and optimizing workflow.

Benefits of having data scientists in maintenance

1. Safer train movement: Data analysis helps notice sudden and unwanted errors in advance

2. Lower Lifecycle Management Costs: Maintenance interval based on the actual situation, up to several tens of percent longer maintenance intervals

3. Improving the usability and reliability of rolling stock: Deviations are noticed in time and can be reacted to before they cause significant damage

4. Increase the efficiency of planning and maintenance: Better demand forecasting, algorithm-based allocation, and optimization of various resources

This article was originally published by VR FleetCare.

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