针对小样本和强噪声条件下的滚动轴承故障诊断问题,提出了一种孪生网络模型:首先,对于滚动轴承故障信号进行连 续小波变换以获得时频图像,引入卷积神经网络模型以实现故障图像模式识别;进而,对故障样本进行交叉配对以重新组合,实 现了少量故障样本的大幅扩容;同时,针对扩容后样本对数据构建了包含两个子模型的孪生网络模型;最后,为了实现强噪声、 小样本条件下滚动轴承故障诊断,设计了孪生网络末端专用分类器,在加噪声数据库和机械故障实验中对方法进行测试,分别 达到了 96. 25% 和 97. 08% 正确率。 所提出模型能够依靠少量样本完成训练并实现轴承故障准确诊断,所需每类样本的数量可 减少至 20 个,与经典卷积神经网络模型相比具有明显优势。
A siamese network model is proposed for fault diagnosis of rolling bearings under small samples and strong noise. First, a series of time-frequency images are obtained from fault signals by the continuous wavelet transform, and the convolutional neural network is introduced to realize the pattern recognition. Secondly, the small samples are recombined with each other to form new sample pairs through cross matching. Thus, the number of fault samples are increased dramatically. Thirdly, a siamese network model including two sub-models is formulated, which uses the new sample pairs. Finally, a new classifier is designed for the siamese network model to realize fault classification with small samples under strong noise. The proposed faulty diagnose method is evaluated by using fault samples from both noise database and experimental measurement. The accuracy vaules are 96. 25% and 97. 08% , respectively. Results show that one fault can be identified by the proposed siamese network model using only 20 samples, which is less than the samples required by CNN model to reach a similar accuracy.