JOURNAL ARTICLE

Rolling bearing fault diagnosis based on VMD-CNN-BiLSTM

Keywords:
Feature extraction Pattern recognition (psychology) Artificial intelligence Computer science Convolutional neural network Robustness (evolution) Fault (geology) Artificial neural network

Metrics

0
Cited By
0.00
FWCI (Field Weighted Citation Impact)
9
Refs
0.01
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Machine Fault Diagnosis Techniques
Physical Sciences →  Engineering →  Control and Systems Engineering
Gear and Bearing Dynamics Analysis
Physical Sciences →  Engineering →  Mechanical Engineering
Engineering Diagnostics and Reliability
Physical Sciences →  Engineering →  Mechanics of Materials

Related Documents

JOURNAL ARTICLE

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

Ling ShaoBing ZhaoX. S. Kang

Journal:   Machines Year: 2025 Vol: 13 (5)Pages: 423-423
JOURNAL ARTICLE

Enhancing Rolling Bearing Fault Diagnosis: A VMD-BILSTM Approach

Liu ZaixuJia MiaoYin FeiHu

Journal:   Zenodo (CERN European Organization for Nuclear Research) Year: 2024
JOURNAL ARTICLE

Enhancing Rolling Bearing Fault Diagnosis: A VMD-BILSTM Approach

Liu ZaixuJia MiaoYin FeiHu

Journal:   Zenodo (CERN European Organization for Nuclear Research) Year: 2024
© 2026 ScienceGate Book Chapters — All rights reserved.