Wheel wear analysis and prediction

CALIPRI Predictor is an easy-to-use cloud storage and analysis tool for CALIPRI measurement devices.

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SIMPLE CONDITION ASSESSMENT OF THE WHEEL SET
FOR PREDICTIVE MAINTENANCE

CALIPRI Predictor is an easy-to-use cloud storage and analysis tool for CALIPRI measurement devices. The data is securely stored, highly available, safe to access and cost effective. By predicting wear levels, the maintenance schedule can be adjusted to the condition of the vehicles. This enables predictable and safe operation of the fleet.

Predictor

ADVANTAGES

CLOSING THE LOOP

  • Smart data handling
  • Fleet condition monitoring
  • Wheel wear analysis and prediction
  • Optimised wheelset maintenance
  • Save railway operation

The CALIPRI Predictor optimizes life-cycle costs by ideal asset utilization and presents the as is condition of the whole fleet in a fast and easy way to the fleet technician, workshop owner, railway maintenance worker or nontechnical personal. Symbolic and colourful CBM.

The cloud based solution offers Data access from all over the world through cloud-based technology. It means you can work at any time and from any end device. It also provides direct leads for a plannable maintenance task- and procurement management. Through Profile Compare technician can analyse the wheel wear evolution of each wheel in your fleet.

These functions are designed for all employees of the company, whether in the workshop, office or home office. Easy to use and yet precise. Together with the measurement data from the CALIPRI measurement systems, actionable information is generated and the feedback loop in railway maintenance can be transformed to be more productive and cost-efficient. Filtered notification for each personal frees up the information channels and an easy share mechanism can be used to connect to 3rd parties.

 

 

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EVENTS

InnoTrans

Berlin (DE)

Hall: 23 | Booth No.: 560

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CONTACT US

We are happy to help you choose the right solution for your measurement task or take care of any repairs. 

 
Funded
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FUNDED BY EUROPEAN UNION

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 726903.

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