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

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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Bibliographic Details
Main Authors: Joaquín Figueroa Barraza, Enrique López Droguett, Marcelo Ramos Martins
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Sensors
Subjects:
feature selection
deep learning
deep neural networks
prognostics and health management
interpretable AI
Online Access:https://www.mdpi.com/1424-8220/21/17/5888

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