Deep learning has evolved to a prevalent approach for machinery fault diagnosis in recent years. However, the high demanding for training data amount refrains its implementation. In this study, we proposed a novel rolling bearing fault diagnosis strategy based on multi-channel convolution neural network(MCNN) combining multi-scale clipping fusion(MSCF) data augmentation technique. The fault signals were augmented using MSCF before transformed to time–frequency images through short-time Fourier transform, then the multi-sensor derived image data were fused by MCNN for feature extraction and fault pattern classification. Experiments validate that the combination of MSCF and MCNN is good at making the best of the information contained in each single sensor recording, leading to a significantly improved fault pattern classification accuracy and cluster effect. The proposed approach is low complexity but effective and robust, it is well suited for bearing fault diagnosis in case limited sensor data and/or variable working condition is presented.

近年来，深度学习已经发展成为一种流行的机械故障诊断方法。然而，对训练数据量的高要求限制了它的实施。在这项研究中，我们提出了一种基于多通道卷积神经网络（MCNN）结合多尺度裁剪融合（MSCF）数据增强技术的新型滚动轴承故障诊断策略。故障信号先使用MSCF增强，然后通过短时傅立叶变换转换为时频图像，然后多传感器导出的图像数据通过MCNN融合进行特征提取和故障模式分类。实验验证了 MSCF 和 MCNN 的组合能够很好地利用每个单个传感器记录中包含的信息，导致故障模式分类精度和聚类效果显着提高。所提出的方法复杂度低，但有效且稳健，非常适合在传感器数据有限和/或工作条件可变的情况下进行轴承故障诊断。