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Author:Mäkelin, Harri
Title:Statistical Learning Based Classification of Rare Events in Predictive Maintenance
Tilastolliseen oppimiseen perustuva harvinaisten tapahtumien tunnistaminen ennakoivassa huollossa
Publication type:Master's thesis
Publication year:2014
Pages:vi + 50      Language:   eng
Department/School:Perustieteiden korkeakoulu
Main subject:Sovellettu matematiikka   (Mat-2)
Supervisor:Salo, Ahti
Instructor:Sutinen, Laura
Electronic version URL: http://urn.fi/URN:NBN:fi:aalto-201507013650
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Location:P1 Ark Aalto  561   | Archive
Keywords:rare events
classification
sampling
ensemble learning
harvinaiset tapahtumat
luokittelu
otanta
ensemble-mallit
Abstract (eng): The detection and correct classification of rare events is a common problem encountered in diverse fields such as fraud prediction, network intrusion detection, churn analysis, medical diagnosis, and predictive maintenance.
In the field of predictive maintenance, anticipating when a piece of equipment will fail is an important aspect of planning maintenance operations.
While such planning can be carried out using sometimes highly sophisticated causal and stochastic models which rely on specialized domain knowledge and the judgement of subject matter experts, in recent years, there has been a shift to a more data driven approach (Choudhary et al., 2009).

The objective of this thesis is to explore machine learning techniques that have been developed to deal with rare event classification problems and to apply them to a real case example using monitoring data from an industrial company with an installed base of several thousands of equipment globally.
The goal is to predict which pieces of equipment are at risk of failing within a specified monitoring period in order to improve the tactical planning of maintenance operations.

The findings of the study suggest rare event prediction methods combined with established machine learning algorithms can be used to improve the accuracy of decision tree based learners in predicting equipment failure.
Abstract (fin): Harvinaisten tapahtumien havainnointia ja oikeaa luokittelua tarvitaan muun muassa petoksen ennakoinnissa, verkkotunkeutumisen havaitsemisessa, asiakkaan uskollisuuden analysoinnissa, lääketieteellisessä diagnosoinnissa ja ennakoivassa huollossa.
Ennakoivan huollon yhteydessä kaluston hajoamisen ennakointi on tärkeää huolto-operaatioiden suunnittelussa.
Vaikka tätä suunnittelua on tyypillisesti lähestytty ongelman sovellusalan asiantuntijoiden tietoon perustuvien kausaalisten ja stokastisten mallien avulla, viime vuosina datakeskeinen lähestymistapa on yleistynyt (Choudhary et al., 2009).

Työn tavoitteena on tutkia harvinaisten tapahtumien luokitteluun soveltuvia menetelmiä ja soveltaa niitä todelliseen esimerkkiin käyttäen monitorointidataa teollisuusyritykseltä, jonka huollettavana on useita tuhansia laitteita eri puolilla maailmaa.
Päämääränä on ennustaa, mitkä laitteet ovat vaarassa hajota määritellyn varoitusajan sisällä huolto-operaatioiden taktisen suunnittelun kehittämiseksi.

Tulokset vahvistavat käsitystä, että harvinaisten tapahtumien tunnistamiseen käytettyjä menetelmiä voidaan käyttää yhdessä vakiintuneiden koneoppimisen tekniikoiden kanssa päätöspuumallien ennustustarkkuuden parantamiseen.
ED:2014-01-29
INSSI record number: 48536
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