ArticleSensors (Basel, Switzerland)2025
A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- ACSE-RNformer: Amplitude-Calibrated Sequence Embedding and Response-Normalized Transformer for Vibration-Based Rotating Machinery Fault Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- Towards a Resilience-Oriented Framework for Fault Diagnosis Under Varying Operating Conditions.Sensors (Basel, Switzerland) · 2026Article
- A CNN Feature Extraction and BKA-Optimized LSSVM Classification Method for Small-Sample Rolling Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- A Bearing Fault Diagnosis Method Based on Weighted Differential Time-Frequency Features and a Dual-Branch Interactive Fusion Network.Sensors (Basel, Switzerland) · 2026Article
- An On-Board Shock Absorber Detection Method for General Aviation Aircraft Landing Gears.Sensors (Basel, Switzerland) · 2026Article
- Prediction of aero engine remaining service life using a bidirectional GRU model with self attention mechanism.Scientific reports · 2026Article
- Comparative analysis of deep learning algorithms for rolling element bearing fault classification under variable loads and speeds.Scientific reports · 2026Article
- Prostate MR image segmentation using a multi-stage network approach.International urology and nephrology · 2026Article
- Rolling bearing fault diagnosis method based on fusion of STFT-statistical features and AL-SOA optimized bagging tree.Scientific reports · 2026Article
- SA-ConSinGAN and reservoir computing fusion for accurate bearing fault classification and severity identification using GAF-based techniques.Scientific reports · 2026Article
- Article
- Adaptive Label Refinement Network for Domain Generalization in Compound Fault Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- Band level similarity reduction and deep dual signal fusion for reliable diagnosis of power insulator defects.Scientific reports · 2025Article
- A Novel Demographic Indicator Fusion Network (DIFNet) for Dynamic Fusion of EEG and Demographic Indicators for Robust Depression Detection.Sensors (Basel, Switzerland) · 2025Article
- Fault diagnosis of rolling bearing failures using a multi-stage e-CNN-GRU-SAM network.Scientific reports · 2025Article
- Fault Diagnosis of Rolling Bearings Based on HFMD and Dual-Branch Parallel Network Under Acoustic Signals.Sensors (Basel, Switzerland) · 2025Article
- The Effect of Data Leakage and Feature Selection on Machine Learning Performance for Early Parkinson's Disease Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Advanced multiscale machine learning for nerve conduction velocity analysis.Scientific reports · 2025Article
- Rolling Based on Multi-Source Time-Frequency Feature Fusion with a Wavelet-Convolution, Channel-Attention-Residual Network-Bearing Fault Diagnosis Method.Sensors (Basel, Switzerland) · 2025Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
This study presents a hybrid deep learning approach for bearing fault diagnosis that integrates continuous wavelet transform (CWT) with an attention-enhanced spatiotemporal feature extraction framework. The model combines time-frequency domain analysis using CWT with a classification architecture comprising multi-head self-attention (MHSA), bidirectional long short-term memory (BiLSTM), and a 1D convolutional residual network (1D conv ResNet). This architecture effectively captures both spatial and temporal dependencies, enhances noise resilience, and extracts discriminative features from nonstationary and nonlinear vibration signals. The model is initially trained on a controlled laboratory bearing dataset and further validated on real and artificial subsets of the Paderborn bearing dataset, demonstrating strong generalization across diverse fault conditions. t-SNE visualizations confirm clear separability between fault categories, supporting the model's capability for precise and reliable feature learning and strong potential for real-time predictive maintenance in complex industrial environments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.