Xing Zhikai, Liu Yongbao, Wang Qiang, Fu Junqiang
Department of Power Engineering, Naval University of Engineering, Wuhan, 430033, Hubei, China.
Sci Rep. 2025 Jan 2;15(1):190. doi: 10.1038/s41598-024-84783-5.
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.
本文提出了一种将迁移学习与ConvNeXt模型相结合的旋转机械故障诊断方法（TL-CoCNN），以应对小样本规模和运行条件变化等挑战。为了在最小化特征损失的同时满足模型的输入要求，引入了一种可视化振动数据的替代方法。具体而言，从原始信号的时域、频域和时频域表示合成RGB图像，随后将其用作输入数据集。故障诊断过程利用在ImageNet数据集上预训练的ConvNeXt模型，并使用合成的RGB图像对其参数进行微调，以执行故障分类任务。实验结果表明，与传统的时域和频域技术相比，这种数据可视化方法无需增加样本规模就能提取更多与故障相关的信息。在多个测试数据集上，从训练时间和模型规模方面评估时，TL-CoCNN模型具有更高的识别准确率。作为一种端到端的故障诊断系统，TL-CoCNN显著增强了复杂信号的特征表示能力，在故障检测与诊断的实际应用中显示出了广阔的前景。
