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Author:Repo, Matti
Title:Data-driven condition monitoring of forest harvester heads
Harvestipään datapohjainen kunnonvalvonta
Publication type:Master's thesis
Publication year:2008
Pages:viii + 79      Language:   eng
Department/School:Matematiikan ja systeemianalyysin laitos
Main subject:Systeemitekniikka   (AS-74)
Supervisor:Koivo, Heikki
Instructor:Hölttä, Vesa
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Reading digital theses in the closed network of the Aalto University Harald Herlin Learning Centre

In the closed network of Learning Centre you can read digital and digitized theses not available in the open network.

The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/

You can read theses on the Learning Centre customer computers, which are available on all floors.

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Opening a thesis

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    Aalto Thesis Database

  • Click on the icon to search for and open the thesis you are looking for from Aaltodoc database. You can find the thesis file by clicking the link on the OEV or OEVS field.

Reading the thesis

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Location:P1 Ark TF80     | Archive
Keywords:condition monitoring
forest harvester
machine learning
performance index
kunnonvalvonta
metsäkone
koneoppiminen
suorituskykyindeksi
Abstract (eng): The costs for sensors, computation and data storage have decreased at the same time as their capabilities have greatly increased.
This has Ied to a situation where modern engineering systems collect huge amounts of measurement data.

One essential way for utilizing the data is in data-based condition monitoring.
The thesis gives an introduction to these methods and applies them to the condition monitoring of forest harvester heads, the subsystem of the harvester responsible for the feeding, delimbing, sawing and measurement operations done on the tree stems.
The monitoring methods will use scalar indices for inputs.
These indices measure the performance of the subsystems and functions of the harvester, and the thesis includes a detailed explanation of the index computation.

The introduced methods are based on common machine learning algorithms, and the theory parts of the thesis will emphasize the particular ways the algorithms can be applied in data-based condition monitoring.
Also elementary ensemble learning approaches will be utilized to apply the underlying methods in a combined fashion.
The thesis uses a benchmarking approach in which a common test scenario built upon artificial fault data will be used for comparing the monitoring algorithms in the harvester head application.

The exposition will give more emphasis on the methods and their justification, rather than the technical details of the harvester head application.
Therefore the developments can be applicable to any modular engineering system with extensive instrumentation for collecting measurements.
Abstract (fin): Mittaustekniikan, laskennan, ja datan varastoinnin kustannukset ovat pienentyneet samalla kun niiden kyvyt ovat lisääntyneet.
Tämä on johtanut tilanteeseen, jossa monet tekniset systeemit kokoavat suuria määriä mittausdataa.

Kasvava datamäärä mahdollistaa monien datapohjaisten kunnonvalvontamenetelmien soveltamisen.
Työ sisältää teoreettisen johdannon näihin menetelmiin, ja tarkastelee sovelluksena harvesteripään kunnonvalvontaa.
Harvesteripää on metsäkoneen osasysteemi, joka vastaa puunrunkojen syöttämisestä, sahaamisesta, oksien karsimisesta, sekä mittausoperaatioista.
Metsäkonesovelluksessa kunnonvalvontamenetelmien lähtömuuttujina käytetään skalaarisia indeksejä, jotka mittaavat metsäkoneen osasysteemien ja toimintojen tehokkuutta.
Indeksien määrittely ja laskenta esitetään yksityiskohtaisesti.

Työssä käytetyt laskennalliset menetelmät pohjautuvat keskeisiin ja tunnettuihin koneoppimisalgoritmeihin.
Esitys pyrkii painottamaan erityisesti näiden algoritmien soveltamista kunnonvalvontatilanteisiin.
Menetelmien vertailuun käytetään testilähestymistä, jossa menetelmiä sovelletaan samaan kattavaan keinotekoiseen vikadataan pohjautuvaan testiskenaarioon.

Työ keskittyy esittelemään datapohjaisten kunnonvalvontamenetelmien taustalla olevia ideoita.
Metsäkonesovelluksen teknisiä yksityiskohtia ei sen sijaan juurikaan käsitellä.
Esitys soveltuu siten monien muidenkin monimutkaisten teknisten systeemien kunnonvalvontaan.
ED:2009-05-08
INSSI record number: 37380
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