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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Vydané v:	Advanced engineering informatics Ročník 54; s. 101778
Hlavní autori:	Hajgató, Gergely, Wéber, Richárd, Szilágyi, Botond, Tóthpál, Balázs, Gyires-Tóth, Bálint, Hős, Csaba
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.10.2022
Predmet:	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
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