Call for startups and SMEs working with Big Data: Predictive maintenance models for industrial robots in body shop

The robots have different applications, such as material handling, spot and stud welding, adhesive dispensing and high resolution inline measurement. The model will be created with the data provided from the robot.
Applications are closed
a year ago

Obtaining working models of the robot from the new Body Shop in VW Navarra. The robots have different applications, such as material handling, spot and stud welding, adhesive dispensing and high resolution inline measurement. The model will be created with the data provided from the robot. These data are variables used in the robot control, for example, load, position, speed, acceleration, torque, motor current of each axis. The parameters of the Tool Centre Point and machine data will be also included in the calculated model.

The model is necessary to:

  • Know working variables in trouble-free situations.
  • Predict mechanic wear and failure.
  • Recognize variable diversions in relation to the model.
  • Check robot mechanical conditions.
  • Robot diagnosis after collision or crash

Data

The challenge has the following sample datasets available for download

Expected outcomes

  • Making planned maintenance interventions before having a production interruption due to a robot failure. That means to change a corrective maintenance for a predictive maintenance. In case of failure or collision check the mechanical conditions of the robot for planning next maintenance interventions.
  • The savings in the plant are still unknown and would like to use the historical data to validate our assumptions and check the amounts that could be saved on this behalf thanks to predictive maintenance.
Deadline for submission is on 27th of June 2018 at 12:00 (noon) 
 
Source: European Data Incubator
 

Illustration Photo: Mobile Industrial Robot (MiR-robot) for internal transportation and logistics (credits: VTT Technical Research Centre of Finland / Flickr Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Generic (CC BY-NC-ND 2.0))

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