Raising rotational speed is an effective way to improve power density of axial piston pumps, but high rotational speed tends to cause undesirable cavitation in the pump. Although some machine learning methods have been successfully applied to detect the cavitation with high accuracy, these conventional methods suffer from the drawback of time-consuming and experience-dependent manual feature extraction. In this paper, a new model based on 1-D convolutional neural network (CNN) is proposed to recognize the cavitation intensity of axial piston pumps. To improve the recognition accuracy under noisy environment, the 1-D CNN receives multi-channel vibration data instead of single-channel data. The experimental results show that the proposed anti-noise 1-D CNN model with multi-channel inputs can achieve 15% higher recognition accuracy than its counterpart with single-channel input on a testing set with SNR = 5 dB.

提高转速是提高轴向柱塞泵功率密度的有效方法，但是高转速往往会在泵中引起不良的气穴现象。尽管一些机器学习方法已成功地应用于高精度检测空化，但是这些常规方法具有耗时且依赖于经验的手动特征提取的缺点。本文提出了一种基于一维卷积神经网络（CNN）的模型来识别轴向柱塞泵的气穴强度。为了提高嘈杂环境下的识别精度，一维CNN接收多通道振动数据而不是单通道数据。