小样本下基于孪生神经网络的柱塞泵故障诊断

针对目前基于深度神经网络的柱塞泵故障诊断方法在小样本条件下精度低、模型欠拟合问题，提出一种小样本条件下基于孪生神经网络的柱塞泵故障诊断方法。搭建了柱塞泵故障实验台，采集柱塞泵在不同健康状态下的壳体振动信号；使用由卷积层和池化层组成孪生子网络自适应地从原始振动信号中提取低维特征，使用欧式距离判定输入样本对的特征相似度；通过相似度对比的方法扩大训练样本数量并训练孪生神经网络模型；最后，对测试样本进行健康状态识别。实验结果表明：与传统深度神经相比，所提方法在小样本情况下具有更高的准确率。同时，多通道数据融合实验表明：所提方法能够从不同通道的信号中学习到有关故障信息，多通道数据融合可以进一步提高诊断准确率。

Piston pump fault diagnosis based on Siamese neural network with small samples

Aiming at the problems of low accuracy and under-fitting in current fault diagnosis methods for piston pumps based on deep neural networks with small samples, a new fault diagnosis method for piston pumps based on Siamese neural networks was proposed. A test bench for piston pumps was built to collect the vibration signals of the pump housing under different health states. The convolution layers and pooling layers were used to construct the Siamese sub network and adaptively extract low-dimensional features from the raw vibration signals. The similarity of the input sample pairs was determined by Euclidean distance to expand training samples, train the Siamese neural network model. And finally identify the health states on the testing dataset. Experimental results demonstrate that compared with traditional deep neural networks, the proposed method has higher diagnosis accuracy with small samples. In addition, data fusion experiments show that the proposed method can learn relevant fault information from signals in different channels, which can improve the accuracy of the fault diagnosis.