Modelling of field experience data

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Dagnelund, Daniel
dc.contributor.advisor Naderi, Davood Neupokoeva, Aleksandra 2016-12-22T11:15:18Z 2016-12-22T11:15:18Z 2016-12-12
dc.description.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. en
dc.format.extent 51+5
dc.language.iso en en
dc.title Modelling of field experience data en
dc.type G2 Pro gradu, diplomityö fi Perustieteiden korkeakoulu fi
dc.subject.keyword time-series en
dc.subject.keyword pattern recognition en
dc.subject.keyword segmentation en
dc.subject.keyword clustering en
dc.subject.keyword operational monitoring en
dc.identifier.urn URN:NBN:fi:aalto-201612226266
dc.programme.major Machine Learning and Data Mining fi
dc.programme.mcode SCI3044 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Karhunen, Juha
dc.programme Master’s Programme in Computer, Communication and Information Sciences fi
dc.ethesisid Aalto 6100
dc.location P1

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