Remaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTM

Remaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTM

Predicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address the problem that Convolutional Neural Networks (CN...

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Bibliographic Details
Main Authors: Bo Sun, Wenting Hu, Hao Wang, Lei Wang, Chengyang Deng
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Sensors
Subjects:
convolutional neural network
Convolutional Block Attention Module
deep learning
rolling bearing
remaining service life prediction
Online Access:https://www.mdpi.com/1424-8220/25/2/554
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