Master Thesis - Predictive maintenance using machine learning on time series data from connected cameras i Lund

Om tjänsten

Category: Computer Science

Scope: 2 students completing 30 credits (20 weeks) each.

Background
The number of IoT devices is growing, resulting in an increased demand for maintenance and an opportunity for continuous and automated device health monitoring. Forecasting the need for maintenance is referred to as predictive maintenance and it is a growing application of machine learning (ML). The forecasts are used to increase the efficiency of maintenance work, as well as to reduce downtime of critical equipment.

Axis has extensive time series (TS) data on connected cameras and several metrics that might be indicative of the maintenance need of a camera individual, such as image quality, process specific memory and CPU consumption, as well as the temperature and power consumption of various components. The data is appropriate for multivariate TS forecasting using ML algorithms and for a comparison of shallow ML algorithms with recurrent neural networks.

Goal
The goal of the diploma work is two fold:

•  Quantitatively assess the relevance of the TS data to related predictive maintenance tasks.
• Explore the achievable accuracy of multivariate forecasting methods using the existing TS data

OK, I am interested! What do I do now?

• You are valuable to us – how nice that you are interested in one of our proposals! There are a few things for you to keep in mind when applying.
• Applications are accepted in both Swedish and English, and you apply via the proposal advert.
• The announced theses are open only to students affiliated with a Swedish university/college either directly or via an exchange program.
• When the thesis proposal states that it includes two students working together, we would like you to apply in pairs. In these cases, send one application each but make sure to clearly state in your application who your co-applicant is. If you have any questions regarding this, please do not hesitate to contact us.
• Please attach your CV and University/college grade summary. 

Contact information
For information or questions regarding technical issues or practical questions, please contact our Master Thesis Coordinator: ola.soder@axis.com

Supervisors - Professor Kalle Åström (LU) and Professor Bo Bernhardsson (LU)

Extra information

Status

Stängd

Ort

Lund

Typ av kontrakt

Examensarbete

Typ av jobb

IT

Körkort önskas

Nej

Tillgång till bil önskas

Nej

Personligt brev krävs

Nej