Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks

In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models results in resistance towards their deployment. Dee...

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Hlavní autoři:	Joaquín Figueroa Barraza, Enrique López Droguett, Marcelo Ramos Martins
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2021-09-01
Edice:	Sensors
Témata:	feature selection deep learning deep neural networks prognostics and health management interpretable AI
On-line přístup:	https://www.mdpi.com/1424-8220/21/17/5888
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