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| Main Authors: | , |
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| Format: | Recurso digital |
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Zenodo
2026
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.19914833 |
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Table of Contents:
- In the contemporary industries, predictive maintenance is critical to avert expensive machinery breakdown due to bearing failures. Nonetheless, standard machine learning models like KNN and Decision Tree cannot represent intricate spatial and temporal characteristics of vibration signals, and therefore have low generalization. In order to resolve this issue, a hybrid CNN-LSTM model is suggested, and CNN is used to retrieve local features, and LSTM is used to learn temporal dependencies. The model is trained on CWRU dataset with the help of normalized vibration signals. Experimental findings indicate that the proposed model has an accuracy of 96.00, precision of 95, recall of 94, and F1-score of 95.00, the highest accuracy, and slightly higher than KNN (71.34% accuracy), Decision Tree (95.69) and ResNet-50 + SVM (95.51). The model also experiences steady convergence with the accuracy rising to 97% and the loss decreasing to 0.11. Altogether, the suggested solution offers a strong, effective and scalable predictive maintenance in real-time.