目的 针对包装机械设备中滚动轴承应用场景多且有效故障数据难采集而导致的智能诊断方法诊断准确率较低的问题，提出一种基于数据增强的滚动轴承智能诊断方法。方法 首先根据轴承振动信号的故障特征，提出一种数据增强方法，有效扩充训练数据样本多样性。然后采用卷积神经网络对原始样本和增强样本进行故障诊断训练，从而大幅度提高诊断模型的诊断性能。为了验证所提方法的有效性，建立滚动轴承故障试验台并采集轴承故障数据。结果 实验结果表明，在标签训练样本不充足的情况下，提出的方法与不使用数据增强方法相比，模型在诊断准确率方面取得了较大的提高，能够准确地识别各类轴承故障。结论 该方法实现了准确地对稀缺标记样本下滚动轴承故障的诊断，为保证包装机械滚动轴承故障诊断的诊断精度提供了可靠的方法。

Aiming at the problem of low diagnostic accuracy of the intelligent diagnosis method caused by many application scenarios of rolling bearings in packaging machinery and the difficulty of collecting effective fault data, a data-enhanced intelligent diagnosis method of rolling bearings is proposed. First, according to the fault characteristics of bearing vibration signals, a data enhancement method was proposed to effectively expand the diversity of training data samples. Then, the convolutional neural network was used to train the original samples and enhanced samples for fault diagnosis, so as to greatly improve the diagnosis performance of the diagnosis model. In order to verify the effectiveness of the proposed method, a rolling bearing failure test rig was established and bearing failure data were collected. The experimental results show that when the label training samples are insufficient, the proposed method has a greater improvement in diagnostic accuracy than the method without data enhancement, and can accurately identify various bearing faults. This method realizes the accurate fault diagnosis of the rolling bearing under the scarce marked samples, and provides a reliable method for ensuring the diagnosis accuracy of the rolling bearing fault diagnosis of the packaging machinery.