Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life

Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life

This paper proposes two deep learning methods for remaining useful life (RUL) prediction of bearings. The methods have the advantageous end-to-end property that they take raw data as input and generate the predicted RUL directly. They are TSMC-CNN, which stands for the time series multiple channel c...

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Bibliographic Details
Main Authors: Jehn-Ruey Jiang, Juei-En Lee, Yi-Ming Zeng
Format: Article
Language:English
Published: MDPI AG 2019-12-01
Series:Sensors
Subjects:
bearing
convolutional neural network
deep learning
long short-term memory
remaining useful life
time series
Online Access:https://www.mdpi.com/1424-8220/20/1/166

Internet

https://www.mdpi.com/1424-8220/20/1/166

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