Lifetime prediction of sealing component using machine learning algorithms
(2016) MIO920Production Management
- Abstract
- Tetra Pak is a world leader in the food packaging industry and has been so for a
very long time. In recent years however, they are experiencing increased competition
from low-cost suppliers selling their previously patented paper as a commodity.
This has forced Tetra Pak to focus more on selling complete systems and services.
One such potential service is condition monitoring coupled with predictive maintenance
of their packaging machines. In a packaging machine, there are electrical
components called inductors that are used for sealing packages.
In this thesis, a model for predicting the remaining useful life of an inductor
is built. Around 8 months of high resolution data is analysed and processed. The
primary tool for data... (More) - Tetra Pak is a world leader in the food packaging industry and has been so for a
very long time. In recent years however, they are experiencing increased competition
from low-cost suppliers selling their previously patented paper as a commodity.
This has forced Tetra Pak to focus more on selling complete systems and services.
One such potential service is condition monitoring coupled with predictive maintenance
of their packaging machines. In a packaging machine, there are electrical
components called inductors that are used for sealing packages.
In this thesis, a model for predicting the remaining useful life of an inductor
is built. Around 8 months of high resolution data is analysed and processed. The
primary tool for data processing is Matlab, and the predictive model is built using
Machine Learning algorithms in Microsoft’s analytics software Azure. In the data
there are clear and visible trends of the inductor degenerating, but the precision
of the predictive model is far too low to be useful in any real world-world scenario
- more data is probably needed. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8895552
- author
- Jansson, Olof
- supervisor
- organization
- course
- MIO920
- year
- 2016
- type
- M1 - University Diploma
- subject
- keywords
- Analytics, Machine Learning, Microsoft Azure, Condition Monitoring, Predictive Maintenance
- other publication id
- 16/5558
- language
- English
- id
- 8895552
- date added to LUP
- 2016-11-30 13:32:30
- date last changed
- 2016-11-30 13:32:30
@misc{8895552, abstract = {{Tetra Pak is a world leader in the food packaging industry and has been so for a very long time. In recent years however, they are experiencing increased competition from low-cost suppliers selling their previously patented paper as a commodity. This has forced Tetra Pak to focus more on selling complete systems and services. One such potential service is condition monitoring coupled with predictive maintenance of their packaging machines. In a packaging machine, there are electrical components called inductors that are used for sealing packages. In this thesis, a model for predicting the remaining useful life of an inductor is built. Around 8 months of high resolution data is analysed and processed. The primary tool for data processing is Matlab, and the predictive model is built using Machine Learning algorithms in Microsoft’s analytics software Azure. In the data there are clear and visible trends of the inductor degenerating, but the precision of the predictive model is far too low to be useful in any real world-world scenario - more data is probably needed.}}, author = {{Jansson, Olof}}, language = {{eng}}, note = {{Student Paper}}, title = {{Lifetime prediction of sealing component using machine learning algorithms}}, year = {{2016}}, }