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Author:Lehtonen, Juuso
Title:Nosturikomponenttien vikaantumisen ennustaminen ja luotettavuus
Crane components' failure prediction and reliability
Publication type:Master's thesis
Publication year:2010
Pages:86 s. + liitt.      Language:   fin
Department/School:Elektroniikan, tietoliikenteen ja automaation tiedekunta
Main subject:Systeemitekniikka   (AS-74)
Supervisor:Koivo, Heikki
Instructor:Sunio, Juha
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
Instructions

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.

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Location:P1 Ark Aalto  5098   | Archive
Keywords:failure
reliability
censored data
probability
maintenance
vikaantuminen
luotettavuus
sensuroitu data
todennäköisyys
kunnossapito
Abstract (eng): Maintenance planning of crane require all estimate or a prediction on components' failures.
In this thesis, different methods for predicting are considered.
Prediction is discussed in more detail in a situation where there are no measurements from components.

Since it is impossible to know exactly when a component will fail probabilities have to be considered.
Most common probability distributions and their features are presented.
In addition, stresses effecting on components and how they could be taken into account in calculating the component reliability is discussed.

Parameter estimation methods least-square and maximum likelihood are presented.
The latter has the advantage that it can be used with censored data.
In the thesis Bayesian inference is also discussed and how it can be used to take advantage of previous knowledge for updating the model.
Finally, the reliability model for an example component is created based on expert opinions.
Abstract (fin): Nosturin kunnossapidon suunnittelu vaatii arviota tai ennustetta komponenttien vikaantumisista.
Tässä diplomityössä tarkastellaan tapoja ennustaa vikaantuminen.
Ennustamista käsitellään tarkemmin tilanteessa, jossa komponenteista ei ole mittauksia.

Koska vikaantumisajankohtaa, on mahdotonta tietää tarkasti. joudutaan tarkastelemaan todennäköisyyksiä.
Työssä esitellään yleisesti käytettyjä vikaantumisjakaumia ja niiden ominaisuuksia.
Jakaumien lisäksi työssä tarkastellaan tapaa, jolla komponenttiin kohdistuvia kuormituksia voidaan mallintaa ja ottaa huomioon komponentin luotettavuutta laskettaessa.

Mallien parametrien estimoinneista esitellään pienimmän neliösumman menetelmä (PNS-menetelmä) sekä suurimman uskottavuuden menetelmä.
Jälkimmäisen etuna on. että sillä voidaan paremmin ottaa huomioon epätäydellinen eli sensuroitu data.
Lisäksi työssä tarkastellaan, miten Bayesilaisella päättelyllä voidaan aikaisempaa tietoa hyödyntää mallin päivittämisessä.
Lopuksi esimerkkikomponentille luodaan luotettavuusmalli asiantuntija-arviointien avulla.
ED:2010-12-21
INSSI record number: 41469
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