Fault diagnosis of rotating parts integrating transfer learning and ConvNeXt model

This paper proposes a fault diagnosis method for rotating machinery that integrates transfer learning with the ConvNeXt model (TL-CoCNN), addressing challenges such as small sample sizes and varying operating conditions. To meet the input requirements of the model while minimizing feature loss, an alternative approach to visualizing vibration data is introduced. Specifically, RGB images are synthesized from time-domain, frequency-domain, and time-frequency domain representations of the original signal, which are subsequently used as the input dataset. The fault diagnosis process leverages a pre-trained ConvNeXt model, initially trained on the ImageNet dataset, and fine-tunes its parameters using the synthesized RGB images to perform the fault classification task. Experimental results demonstrate that this data visualization method extracts more fault-related information compared to traditional time-domain and frequency-domain techniques, without the need to augment the sample size. The TL-CoCNN model achieves superior recognition accuracy when evaluated in terms of training time and model size across multiple test datasets. As an end-to-end fault diagnosis system, TL-CoCNN significantly enhances the feature representation capability of complex signals, showing promising potential for practical applications in fault detection and diagnosis.