[en] Predictive Maintenance is the maintenance methodology that provides the best performance to industrial organisations in terms of time, equipment effectiveness and economic savings. Thanks to the recent advances in technology, capturing process data from machines and sensors attached to them is no longer a challenging task, and can be used to perform complex analyses to help with maintenance requirements. On the other hand, knowledge of domain experts can be combined with information extracted from the machines' assets to provide a better understanding of the underlying phenomena. This thesis proposes a methodology to assess the different requirements in relation to Predictive Maintenance. These are (i) Anomaly Detection (AD), (ii) Root Cause Analysis (RCA) and (iii) estimation of Remaining Useful Life (RUL). Multiple machine learning techniques and algorithms can be found in the literature to carry out the calculation of these requirements. In this thesis, the Attribute Oriented Induction (AOI) algorithm has been adopted and adapted to the Predictive Maintenance methodology needs. AOI has the capability of performing RCA, but also possibility to be used as an AD system. With the purpose of performing Predictive Maintenance, a variant, Repetitive Weighted Attribute Oriented Induction (ReWAOI ), has been proposed. ReWAOI has the ability to combine information extracted from the machine with the knowledge of experts in the field to describe its behaviour, and derive the Predictive Maintenance requirements. Through the use of ReWAOI, one-dimensional quantification function from multidimensional data can be obtained. This function is correlated with the evolution of the machine's wear over time, and thus, the estimation of AD and RUL has been accomplished. In addition, the ReWAOI helps in the description of failure root causes. The proposed contributions of the thesis have been validated in different scenarios, both emulated but also real industrial case studies.

[eu] Enpresei errendimendu hoberena eskaintzen dien mantentze metodologia Mantentze Prediktiboa da, denbora, ekipamenduen eraginkortasun, eta ekonomia alorretan. Azken urteetan eman diren teknologia aurrerapenei esker, makina eta sensoreetatiko datuen eskuraketa jada ez da erronka, eta manentenimendurako errekerimenduak betetzen laguntzeko analisi konplexuak egiteko erabili daitezke. Bestalde, alorreko jakintsuen ezagutza makinetatik eskuratzen den informazioarekin bateratu daiteke, gertakarien gaineko ulermena hobea izan dadin. Tesi honetan metodologia berri bat proposatzen da, Mantentze Prediktiboarekin lotura duten errekerimenduak betearazten dituena. Ondorengoak dira: (i) Anomalien Detekzioa (AD), (ii) Erro-Kausaren Analisia (RCA), eta (iii) Gainontzeko Bizitza Erabilgarriaren (RUL) estimazioa. Errekerimendu hauen kalkulua burutzeko, ikasketa automatikoko hainbat algoritmo aurkitu daitezke literaturan. Tesi honetan Attribute Oriented Induction (AOI) algoritmoa erabili eta egokitu da Mantentze Prediktiboaren beharretara. AOI-k RCA estimatzeko ahalmena dauka, baina AD kalkulatzeko erabilia izan daiteke baita ere. Mantentze Prediktiboa aplikatzeko helburuarekin, AOI-rentzat aldaera bat proposatu da: Repetitive Weighted Attribute Oriented Induction (ReWAOI ). ReWAOI-k alorreko jakintsuen ezagutza eta makinetatik eskuratutako informazioa bateratzeko ahalmena dauka, makinen portaera deskribatu ahal izateko, eta horrela, Mantentze Prediktiboaren errekerimenduak betetzeko. ReWAOI-ren erabileraren ondorioz, dimentsio bakarreko kuantifikazio funtzioa eskuratu daiteke hainbat dimentsiotako datuetatik. Funztio hau denboran zehar makinak duen higadurarekin erlazionatuta dago, eta beraz, AD eta RUL-aren estimazioak burutu daitezke. Horretaz gain, ReWAOI-k hutsegiteen erro-kausaren deskribapenak eskaintzeko ahalmena dauka. Tesian proposatutako kontribuzioak hainbat erabilpen kasutan balioztatu dira, batzuk emulatuak, eta beste batzuk industria alorreko kasu errealak izanik.