ArticleScientific reports2025
An efficient bearing fault detection strategy based on a hybrid machine learning technique.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Comparative analysis of deep learning algorithms for rolling element bearing fault classification under variable loads and speeds.Scientific reports · 2026Article
- Fault Diagnosis of Rotating Machinery Using Supervised Machine Learning Algorithms with Integrated Data-Driven and Physics-Informed Feature Sets.Sensors (Basel, Switzerland) · 2026Article
- Multi-objective sizing and location of DG in distribution network by hybrid gorilla troops optimization-genetic algorithm: a real case study.Scientific reports · 2026Article
- Research on bearing fault diagnosis based on machine learning and SHAP interpretability analysis.Scientific reports · 2025Article
- Electric torque for fault severity diagnosis in gearbox-type load: ML approaches based on Poincaré plot and statistical features.Scientific reports · 2025Article
- Hybrid deep learning framework for real-time fault detection in squirrel-cage induction motors.PloS one · 2025Article
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Authors and funding
9 authors.
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Abstract
This study introduces an innovative method for addressing the bearing fault detection problem in rotating machinery. The proposed approach integrates multi-feature extraction, advanced feature selection, and state-of-the-art classification techniques using convolutional neural network (CNN) models. Leveraging the comprehensive Fault Bearing Dataset from Case Western Reserve University (CWRU), continuous wavelet transforms (CWT) and CNNs are utilized for feature extraction. The methodology also incorporates machine learning model tuning through Tree-Structured Parzen Estimators (TPE) for optimal hyperparameter adjustment, ensuring high-performance classification. Experimental results, based on the ResNet-50-SVM hybrid model, showed the effectiveness of the proposed approach in achieving an impressive accuracy of 95.51%. This confirms that the proposed methodology represents a significant advancement in bearing fault detection, providing an effective solution for predictive and preventive maintenance in industrial applications.
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Registered trials
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