Implementation of MF block in CNN for advanced REB fault diagnosis

Rolling element bearings (REBs) are crucial components in various industrial applications. The bearing faults occur due to prolonged operation, overloading, high speed, and inadequate lubrication. A bearing failure can lead to significant downtime and huge maintenance costs for the machines. Hence, industries require condition monitoring to reduce costs. This study presents an automated detection approach for diagnosing faults in REBs using a Customized Convolutional Neural Network (C-CNN). This work focuses on vibration signals sampled at 12,800 Hz and 5120 Hz as input data to perform the fault diagnosis of bearings. Further, the Multi Feature (MF) block has been used in the architecture of the C-CNN model for better accuracy. By incorporating techniques such as batch normalization and dropout, the model has improved stability and prevented overfitting. Using the Balance Cross-Entropy (BCE) loss function for training has helped the model optimize prediction accuracy by minimizing the difference between the actual class and the predicted class probabilities. A comparison with models such as MSCNN, CNN, RF, DBN, KNN, ANN, SVM, LSTM, ResNet and SqueezeNet was carried out. The proposed C-CNN model has been found to well perform other classifiers in accurately recognizing bearing faults, achieving an excellent accuracy of 95% and 93.5% on 12,800 and 5120 Hz datasets, respectively. Statistical significance tests and error bars validate the robustness of the model's performance. Extensive experiments were conducted to evaluate the impact of sampling frequency on diagnostic accuracy, hyperparameter tuning strategies, and model robustness under different noise levels and operating conditions. Furthermore, computational complexity analysis, including FLOPs estimation, was performed to assess real-time applicability. The findings indicate that the C-CNN approach is a reliable and efficient solution for bearing fault classification, offering significant practical implications for industrial condition monitoring systems and helping to prevent plant shutdown.