PredMaX: Predictive maintenance with explainable deep convolutional autoencoders

A novel data exploration framework (PredMaX) for predictive maintenance is introduced in the present paper. PredMaX offers automatic time period clustering and efficient identification of sensitive machine parts by exploiting hidden knowledge in high-dimensional, unlabeled temporal data. Condition m...

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Bibliographic Details

Published in:	Advanced engineering informatics Vol. 54; p. 101778
Main Authors:	Hajgató, Gergely, Wéber, Richárd, Szilágyi, Botond, Tóthpál, Balázs, Gyires-Tóth, Bálint, Hős, Csaba
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.10.2022
Subjects:	Automatic cluster identification Deep convolutional autoencoder Explainable artificial intelligence Explainable neural networks Predictive maintenance Unsupervised learning Explainable artificial intelligence Automatic cluster identification Unsupervised learning Explainable neural networks Predictive maintenance Deep convolutional autoencoder
ISSN:	1474-0346, 1873-5320
Online Access:	Get full text
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