Adaptive Study of Convolutional Neural Networks Based on Transfer Learning in Mechanical Intelligent Fault Diagnosis

This study proposes an improved fault diagnosis model on the grounds of transfer learning and convolutional neural networks for addressing the issue of difficulty in accurately labeling and extracting fault signals in mechanical systems. By utilizing boundary self balancing adversarial generation networks and denoising autoencoders to generate signals that match the distribution of real system signals, and combining with dynamic models, efficient classification of real signals is achieved. The model was trained and tested on several publicly available bearing datasets, including the Case Western Storage Bearing Dataset, the Self collected Bearing Dataset, and the Institute of Maintenance Science Bearing Dataset. The fault diagnosis model on the ground of transfer learning and convolutional neural networks performs well in different dataset transfer scenarios, especially in A → C scenarios, with a prediction accuracy of 98.07% and an average accuracy of 93.83%. This study indicates that the proposed fault diagnosis model on the ground of transfer learning and improved convolutional neural networks has high accuracy and good domain adaptation ability inmechanical intelligent fault diagnosis. This demonstrates excellent performance and significant application value.