Automated Fault Diagnosis of Rolling Bearings Using InceptionV3 Deep Learning Model for Enhanced Industrial Maintenance

This paper proposes a novel method of diagnosing rolling bearing issues with the InceptionV3 algorithm and deep learning techniques. Because rolling bearings are essential to the operation of machinery, their failure can cause significant problems in a wide range of industrial applications. Conventional defect diagnostic methods sometimes need manual examination and specialized knowledge, which can be laborious and prone to errors. Deep learning algorithms, on the other hand, can automatically identify faults since they can extract discriminative characteristics directly from unprocessed image data. In this work, vibration data from rolling bearings are evaluated using the broad feature extraction capabilities of the InceptionV3 convolutional neural network. A sizable dataset of tagged fault examples is used to train the network to discriminate between normal and faulty bearing conditions. The experimental results demonstrate the effectiveness of the suggested method in accurately and precisely detecting different kinds of bearing issues, achieving 98.50% accuracy, 98.80% recall, 97.20% precision, and 97.90% F1-score, which are the best results compared to traditional machine learning methods (e.g., SVM, KNN, Random Forest) and signal processing-based approaches (e.g., FFT, Wavelet Transform). This study offers a viable alternative for increasing equipment reliability and lowering maintenance costs, advancing automated fault diagnostic systems in industrial environments.