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THESIS - Health status of embedded devices installed on trains

Televic Rail · Izegem, West-Vlaanderen, Belgium · Hybrid

  • 201–500 employees
  • Worldwide

Mixed hiring record · median 43 days open

3030

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Open 228 days · How we score

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  • Open 228 days
  • 27% of this employer's openings are over 60 days old

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  • Still listed on the employer's own board

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THESIS - Health status of embedded devices installed on trains at Televic Rail. We’ll keep it in your applied jobs, so you can see it later and be warned before applying to the same employer twice.

About Televic Rail

With over 30 years of experience in designing, manufacturing and maintaining on-board communication and control systems, Televic Rail is a leading, trusted partner for railway operators and train builders worldwide. Its Passenger Information Systems and Control Systems are high quality, tailor-made solutions that offer the flexibility, user-friendliness and stability that our clients ask for. Our various types of on board control systems such as our bogie monitoring systems are innovative, yet reliable products designed specifically for the railway business. Trains and trams all around the world are equipped with Televic Rail solutions, from New Zealand to Canada, from China to the United States, from India to Belgium, England and France.

Your project

The devices we install on trains (audio devices, LED panels, TFT panels) have many sensors onboard which could be used to determine the 'health' of the device, functional operation and possible prediction of remaining lifetime.

This thesis explores a few steps required for such health monitoring:

  • Gather an overview of the 'sensors' and 'diagnostics' that can be gathered on the devices (temperature, light, voltage monitors, cpu load, memory usage, impedance measurement of loudspeaker branches, ...)
  • Determine the method of gathering this info from all devices on a train network
  • Collect data on a set of devices and determine metrics to "predict" the health state of these devices. We could consider taking the data into account of devices which did fail.
  • Verify if these health checks could be run on the device itself.

Extra info

  • Level: Bachelor, Master
  • Domains: AI / Machine Learning, Embedded Software, Software
  • Type of work: Research: 20% - Implementation: 60% - Experimentation: 20%
  • Location: Televic
  • Number of students: 1

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