today 133

Predictive models for maintenance

Predictive Maintenance

Operators, who have worked on “their” machines for many years, know every detail, every sound. They feel when something is wrong. However, where there is a lot of technology in a confined space, it is often difficult to locate approaching problems.

Predictive Maintenance Systems make it possible to predict costly repairs or serious breakdowns. Preventive measures can be taken in good time before significant damage occurs.

For some time now, PlasserDatamatic has allowed for the live monitoring of important data on the working parameters, GPS position, working direction, engine, filling levels and hydraulic pressures on the machines, if desired. This makes it possible to access the current status of entire machine fleets from the office. Providing highly valuable information, the data is stored to enable later access. 

To decode the data and translate it into specific instructions for condition-based maintenance, it has to be analysed. This is uncharted territory in the railway industry. Breaking new ground, P&T Connected is dealing with this.


mainly focuses on recording, preparing and analysing data to make predictive maintenance become a reality for track maintenance machines. Established in January 2017, P&T Connected is the latest subsidiary of Plasser & Theurer. The company has taken on seven members of staff by now. Experts in computer science, physics, mathematics and statistics are part of the new company based at the Hagenberg Campus at the University of Applied Sciences Upper Austria – Austria’s “Silicon Valley”.

Renew wearing parts when it is the most economically viable 

One of many new opportunities is to renew wearing parts when it is the most economically viable. This decision is based on data collected by sensors. They indicate how long wearing parts will continue to work flawlessly. This makes it possible to identify early on when certain parts will have to be exchanged. In addition, downtimes and machine failures can be minimised. This benefits both the machine operator and the infrastructure.

In the past, it was the service technicians who made such decisions based on their experience. Today, these decisions can be made on the basis of reliable data.

Enabling condition-based maintenance for entire machines

This methodology can also be applied to the maintenance of entire machines, taking a decisive step towards condition-based maintenance and away from maintenance at intervals. P&T Connected provides data analy­sis and, on this basis, recommends further actions.

Condition Monitoring

  • reduces life cycle costs
  • helps optimise maintenance strategy costs
  • recommends measures based in dat

The diagram shows that predictive maintenance is particularly important where the impact is high (e.g. machine breakdown, costs, downtimes ...) with low frequency and little to no planning.