Deep learning-based intelligent fault diagnosis methods have attracted increasing attention for their automatic feature extraction ability. However, existing works are usually under the assumption that the training and test dataset share similar distributions, which unfortunately always violates real practice due to the variety of working conditions. In this paper, an end-to-end scheme of joint use of two-direction signals and capsule network (CN) is proposed for fault diagnosis of rolling bearing. With the help of the superior ability of CN in capturing the spatial position information between features, more valuable information can be mined. Aiming to eliminate the influence of different rotational speeds, vertical and horizontal vibration signals are fused as the input to CN, so that invariant features can be extracted automatically from the raw signals. The effectiveness of the proposed method is verified by experimental data of rolling bearing under different rotational speeds and compared with a deep convolutional neural network (DCNN). The results demonstrate that the proposed scheme is able to recognize the fault types of rolling bearing under scenarios of different rotational speeds.

中文翻译：

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基于胶囊网络的滚动轴承在不同转速下的故障诊断方案。

基于深度学习的智能故障诊断方法因其自动特征提取能力而受到越来越多的关注。但是，现有工作通常是在训练和测试数据集共享相似分布的前提下进行的，不幸的是，由于工作条件的多样性，这总是违反了实际操作。提出了一种双向信号与胶囊网络（CN）联合使用的端到端方案，用于滚动轴承的故障诊断。借助CN在捕获要素之间的空间位置信息方面的出色能力，可以挖掘出更多有价值的信息。为了消除不同转速的影响，将垂直和水平振动信号融合为CN的输入，这样就可以从原始信号中自动提取不变特征。通过在不同转速下滚动轴承的实验数据验证了该方法的有效性，并与深度卷积神经网络（DCNN）进行了比较。结果表明，所提出的方案能够识别不同转速下滚动轴承的故障类型。