Modelling of field experience data

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Perustieteiden korkeakoulu | Master's thesis
Ask about the availability of the thesis by sending email to the Aalto University Learning Centre oppimiskeskus@aalto.fi

Authors

Date

2016-12-12

Department

Major/Subject

Machine Learning and Data Mining

Mcode

SCI3044

Degree programme

Master’s Programme in Computer, Communication and Information Sciences

Language

en

Pages

51+5

Series

Abstract

Nowadays sensors networks produce tons of time-series data. Data mining techniques are potentially able to extract knowledge from data. However, sensors network time-series require special treatment in its analysis due to such problems as large volumes of data, noise, incompleteness, and so on. The main idea of this thesis is implementation of data mining techniques in the domain of steam and gas turbine operational data. The goal is to find patterns which could indicate a higher wearing out of the turbine components. Thus, we conduct the whole data mining procedure from extraction of data to interpretation of the results and proposal on the future work directions. We perform preprocessing of time-series, segmentation, clustering and transformation of data to find indicative patterns. We get two new features which potentially can contribute to scrap rate prediction and survival analysis models. We assume that this approach can be extended and used in other levels of research.

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Supervisor

Karhunen, Juha

Thesis advisor

Dagnelund, Daniel
Naderi, Davood

Keywords

time-series, pattern recognition, segmentation, clustering, operational monitoring

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Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-201612226266

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[dipl] Perustieteiden korkeakoulu / SCI