Using machine learning to identify flow regimes from capacitance sensor data
Master thesis
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http://hdl.handle.net/11250/2409094Utgivelsesdato
2016-06-29Metadata
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Sammendrag
In this thesis the k-means clustering and a neural network is developed and used to
classify capacitance data from multi phase flow in a horizontal tube.
Theoretical background for the unsupervised machine learning algorithm: k-means
clustering and for the supervised machine learning algorithm: Neural network
with one hidden layer is presented. Data acquisition method and analysis of the
multi-phase flow data is discussed. The machine learning algorithms are created
in Matlab in a general manner so that the programs will work for input of varying
sizes. The k-means algorithm is used as a method for clustering provided data
examples in flow regime clusters. The algorithm fails to provide rigid clusters which
match observations at phase transitions, but works well as a general indicator of
ow regime clusters. Classifications from the k-means algorithm and a set of manual
classifications is used as input in the neural network for training and testing. The
neural network provides overall good results, and shows its ability to detect complex
patterns.
Beskrivelse
Master's thesis in Petroleum technology