Wisdom of the crowd, is there a single crowd?

From ISLAB/CAISR
Title Wisdom of the crowd, is there a single crowd?
Summary Finding different clusters of machines based on their behavior as a first step towards anomaly detection and predictive maintenance.
Keywords Predictive maintenance, anomaly detection, clustering.
TimeFrame Fall 2018
References
Prerequisites Good knowledge of applied data science: supervised and unsupervised learning.
Author
Supervisor Pablo del Moral, Sławomir Nowaczyk
Level Master
Status Open

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Many anomaly detection methods rely on the so-called wisdom of the crowd: the behavior of the majority is regarded as normal and everything that is not similar to the majority is regarded as an anomaly. In this project you will be working with data coming from a fleet of sterilizers used in hospitals for sterilization of medical equipment. A priori, these machines can have very different configurations and can be used for different purposes, loads, and in different conditions. For any machine learning related task a question has to be answered, how similar are the data coming from different machines?, can we use the data from one machine to predict on another? The data coming from different machines will be different up to some degree, but it is likely that there are groups of machines behaving similarly. The next question is, how different are these groups of machines from each other? The goal for this project is to answer those questions for a real case problem coming from one of our industrial partners.