Rolling Bearing Fault Diagnosis Based on VMD-DWT and HADS-CNN-BiLSTM Hybrid Model

Rolling Bearing Fault Diagnosis Based on VMD-DWT and HADS-CNN-BiLSTM Hybrid Model

This study proposes a hybrid framework for rolling bearing fault diagnosis by integrating a Variational Mode Decomposition–Discrete Wavelet Transform (VMD-DWT) with a Hybrid Attention-Based Depthwise Separable CNN-BiLSTM (HADS-CNN-BiLSTM) to address noise interference and low diagnostic accuracy und...

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Bibliographic Details
Main Authors: Luchuan Shao, Bing Zhao, Xutao Kang
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Machines
Subjects:
signal reconstruction
hybrid attention mechanism
depthwise separable convolution
bidirectional long short-term memory
rolling bearings
fault diagnosis
Online Access:https://www.mdpi.com/2075-1702/13/5/423
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