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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Veröffentlicht in:	Advanced engineering informatics Jg. 54; S. 101778
Hauptverfasser:	Hajgató, Gergely, Wéber, Richárd, Szilágyi, Botond, Tóthpál, Balázs, Gyires-Tóth, Bálint, Hős, Csaba
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.10.2022
Schlagworte:	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-Zugang:	Volltext
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