Detecting different types of machines based on usage

From ISLAB/CAISR
Title Detecting different types of machines based on usage
Summary This project is about studying how can we distinguish among different types of machines based on their usage.
Keywords Data Mining. Data representation
TimeFrame
References 1.- Bengio Y, Courville A, P Vincent P. Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence. Volume: 35, Issue: 8, Aug. 2013.

2.- Kotsiantis S. Supervised Machine Learning: A Review of Classification Techniques. Informatica 31 (2007) 249-268

3.- Grira N, Crucianu M, Boujemaa N. Unsupervised and Semi-supervised Clustering: a Brief Survey.

4.- Taskar B, Segal E, Koller D. Probabilistic Classification and Clustering in Relational Data.

Prerequisites Good knowledge of machine learning and programming skills for implementing machine learning algorithms
Author
Supervisor Sławomir Nowaczyk, Pablo del Moral.
Level Master
Status Open

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In PRIME project we are analyzing data collected from 3016 machines sold by Getinge Group. These machines consist of sterilizers, washers (and possibly something else) running under different control units.

The data available is a time-stamped log of the programs run in every machine and the sequence of phases undergone through these programs; along with other information.

The goal for this project is finding the right data representation to apply supervised and unsupervised machine learning methods in order to complete these tasks based on the usage of the machines:

  1. Separate washers and sterilizers based on their usage.
  2. Separate different types of control units. There are basically two different control units running on the machines.
  3. Separate different models of machines. There are different types and models of machines in the cases of both sterilizers and washers.
  4. Separate different usages of the machines. Similar machines can be operating under different conditions.