Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions

Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions

Abstract Bearing fault diagnosis is of great significance for ensuring the safety of rotating electromechanical equipment. A deep learning network framework for diagnosing bearing faults under multiple load conditions is proposed to address the problems of extracting a single feature scale from bear...

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Bibliographic Details
Main Authors: Zhao Dengfeng, Tian Chaoyang, Fu Zhijun, Zhong Yudong, Hou Junjian, He Wenbin
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
Bearings
Fault diagnosis
CNN
BiLSTM
AM
Transfer learning
Online Access:https://doi.org/10.1038/s41598-025-96137-w
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https://doi.org/10.1038/s41598-025-96137-w

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