Vibration Signal-Based Fault Diagnosis of Rotary Machinery Through Convolutional Neural Network and Transfer Learning Method

Artificial Intelligence (AI) is revolutionizing proactive repair systems by enabling real-time identification of bearing faults in industrial machinery. However, traditional fault detection methods often struggle in dynamic environments due to their dependence on specific training conditions. To address this limitation, a transfer learning (TL)-based methodology has been developed for bearing fault detection, so that the model trained under some specific training conditions can perform accurately under significantly different real-time working conditions, thereby significantly improving diagnostic efficiency while reducing training time. Initially, a deep learning approach utilizing convolutional neural networks (CNNs) has been employed to diagnose faults based on vibration data. After achieving high classification performance at source domain conditions, the performance of the model is re-evaluated by applying it to the Case Western Reserve University (CWRU) dataset as the target domain through the TL method. short-time Fourier transform is employed for signal preprocessing, enhancing feature extraction and model performance. The proposed methodology has been validated across various CWRU dataset configurations under different operating conditions and environments. The proposed approach achieved a 99.7% classification accuracy in the target domain, demonstrating effective adaptability and robustness under domain shifts. The results demonstrate how TL-enhanced CNNs can be used as a scalable and efficient way to diagnose bearing faults in industrial environments.