School of Mechanical Engineering, Ningxia University
Ningxia Key Laboratory of CAE on Intelligent Equipment

基于深度学习的故障诊断方法对数据集的质量有很高要求，需要大批量数据才能进行良好的模型训练，从而实现准确的故障诊断，而在实际应用中能够采集到的故障信号通常很有限。针对托辊故障声音信号获取困难、样本量少，导致智能故障诊断方法性能受限的问题，提出了一种基于小样本声音信号的托辊故障诊断方法。使用特征转换方法将一维声音信号转换为二维时频图像，将频率域的特征融入进来，以提高数据集对故障特征的表达能力；提出了多种类型时频图结合的数据集扩充方法，将短时傅里叶变换（STFT）、连续小波变换（CWT）、希尔伯特−黄变换（HHT） 3种时频分析方法绘制的时频图相结合，以扩充数据集，增加数据样式；引入了深度迁移学习的思想，使用轴承数据集对模型进行预训练，然后使用托辊数据对预训练模型进行微调，以进一步提升模型的识别准确率。实验结果表明：多种类型时频图结合的数据集扩充方法能有效解决使用小样本数据训练模型时易过拟合的问题；使用迁移学习后，模型的测试准确率达98.81%，相较于不使用迁移学习时提升了7%，且没有出现过拟合现象，说明模型训练良好；相较于生成对抗网络扩充STFT时频图数据集+迁移学习的方法，多种类型时频图结合的数据集扩充+迁移学习的方法准确率提高了4%，且更容易实现，可解释性更强。

Fault diagnosis methods based on deep learning have high requirements for the quality of the dataset, requiring a large amount of data for good model training to achieve accurate fault diagnosis. However, the fault signals that can be collected in practical applications are usually limited. A method for diagnosing roller faults based on small sample sound signals is proposed to address the problem of limited performance of intelligent fault diagnosis methods due to the difficulty in obtaining sound signals for roller faults and the small sample size. The feature transformation method is used to convert one-dimensional sound signals into two-dimensional time-frequency images, incorporating features from the frequency domain to improve the dataset's capability to express fault features. A dataset expansion method combining multiple types of time-frequency maps has been proposed. The method combines time-frequency maps drawn by three time-frequency analysis methods: short time fourier transform (STFT), continuous wavelet transform (CWT), and Hilbert Huang transform (HHT) to expand the dataset and increase data styles. The concept of deep transfer learning is introduced, using bearing datasets to pre-train the model, and then using roller data to fine-tune the pre-trained model to further improve the recognition accuracy of the model. The experimental results show that the dataset expansion method combining multiple types of time-frequency maps can effectively solve the problem of overfitting when training models with small sample data. After using transfer learning, the testing accuracy of the model reaches 98.81%, an improvement of 7% compared to not using transfer learning. There was no overfitting phenomenon, indicating that the model is well-trained. Compared to the method of generating adversarial networks to expand the STFT time-frequency map dataset and transfer learning, the method of dataset expansion by combining multiple types of time frequency maps and transfer learning has an accuracy improvement of 4%. It is easier to implement, and has stronger interpretability.

宁夏自然科学基金项目（2021AAC03046）；2023年宁夏回族自治区重点研发计划项目（2023BDE03005) 。

郝洪涛，邱园园，丁文捷. 一种基于小样本声音信号的托辊故障诊断方法[J]. 工矿自动化，2023，49(8)：106-113.

HAO Hongtao, QIU Yuanyuan, DING Wenjie. A fault diagnosis method for roller based on small sample sound signals[J]. Journal of Mine Automation，2023，49(8)：106-113.