Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction f...

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Bibliographic Details
Main Authors: Xiangang Cao, Fuqiang Zhang, Jiangbin Zhao, Yong Duan, Xingyu Guo
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Applied Sciences
Subjects:
multi-domain mixed features
remaining useful life prediction
temporal convolutional networks
feature dimensionality reduction
rolling bearing
Online Access:https://www.mdpi.com/2076-3417/14/6/2354

Internet

https://www.mdpi.com/2076-3417/14/6/2354

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