Machine learning integration to conditional monitoring system in industrial automation: Data clustering case study

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Journal Title

Journal ISSN

Volume Title

Sähkötekniikan korkeakoulu | Master's thesis

Authors

Date

2020-06-15

Department

Major/Subject

Control, Robotics and Autonomous Systems

Mcode

ELEC3025

Degree programme

Master’s Programme in Automation and Electrical Engineering

Language

en

Pages

62 + 2

Series

Abstract

Machine learning applications are becoming more and more popular in numerous sectors of the industry nowadays. Industrial automation in general and factory automation in particular is a big market in which a large number of enterprises have tried to put their footprint onto this highly potential market. Our company - InSolution Oy, like others, found a demand for a machine learning system to integrate into its existing conditional monitoring platform. While a large number of enterprises have implemented their machine learning systems locally, the others have chosen to build with cloud computing platforms. However, generally, those machine learning applications are standalone and rarely integrate into conditional monitoring system. This thesis provides a particular method and solution for integrating machine learning applications into the industrial automation sector based on a case study from InSolution Oy. Also, this project presents a comprehensive comparison among different popular cloud platforms to offer InSolution Oy an overview of the machine learning development feasibility in a cloud platform.

Description

Supervisor

Vyatkin, Valeriy

Thesis advisor

Katajisto, Juha

Keywords

machine learning, integration, conditional monitoring, industrial automation, factory automation, architecture

Other note

Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202006213962

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[dipl] Sähkötekniikan korkeakoulu / ELEC