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Volume 25
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10.3390/s25092712
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Article

A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction

by
Muhammad Farooq Siddique
1,
Faisal Saleem
1,
Muhammad Umar
1,
Cheol Hong Kim
2 and
Jong-Myon Kim
1,*
1
Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
2
School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(9), 2712; https://doi.org/10.3390/s25092712
Submission received: 31 March 2025 / Revised: 23 April 2025 / Accepted: 24 April 2025 / Published: 25 April 2025
(This article belongs to the Special Issue Sensor-Based State Estimation and Fault Diagnosis in Automatic Control)
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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.
Keywords: fault diagnosis; continuous wavelet transform; multi-head self-attention; bidirectional long short-term memory; 1D convolutional residual network fault diagnosis; continuous wavelet transform; multi-head self-attention; bidirectional long short-term memory; 1D convolutional residual network

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MDPI and ACS Style

Siddique, M.F.; Saleem, F.; Umar, M.; Kim, C.H.; Kim, J.-M. A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction. Sensors 2025, 25, 2712. https://doi.org/10.3390/s25092712

AMA Style

Siddique MF, Saleem F, Umar M, Kim CH, Kim J-M. A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction. Sensors. 2025; 25(9):2712. https://doi.org/10.3390/s25092712

Chicago/Turabian Style

Siddique, Muhammad Farooq, Faisal Saleem, Muhammad Umar, Cheol Hong Kim, and Jong-Myon Kim. 2025. "A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction" Sensors 25, no. 9: 2712. https://doi.org/10.3390/s25092712

APA Style

Siddique, M. F., Saleem, F., Umar, M., Kim, C. H., & Kim, J.-M. (2025). A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction. Sensors, 25(9), 2712. https://doi.org/10.3390/s25092712

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