Abstract Considering all the monitoring data of bearings until failure, very few data are acquired when the bearings are faulty. Such circumstance leads to small faulty sample problem when an intelligent fault diagnosis method is applied. A deep neural network trained with small samples cannot be trained completely, and tends to overfit, which results in poor performance in practical application. To solve this problem, a compact convolutional neural network augmented with multiscale feature extraction is proposed in this paper. Multiscale feature extraction unit is introduced to extract features at different time scales without adding convolution layers, which can reduce the depth of the network while ensuring classification ability and alleviating the overfitting problem caused by the network being too complicated. Besides, a specially designed compact convolutional neural network synthetically analyzes the multiscale features. By combing these two tricks, the proposed neural network can extract more sensitive features with a relatively shallow structure, which increases classification accuracy under small samples. Dropout technique is also used to prevent the network from overfitting. Effectiveness of the proposed method is verified by three bearing datasets. Experiments show that this network can achieve competitive results with limited training samples even with different load and mixed rotating speed.

中文翻译：

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用于机械智能故障诊断的采集监测数据的多尺度特征提取增强的紧凑卷积神经网络

摘要 考虑到轴承故障前的所有监测数据，轴承故障时获取的数据很少。这种情况在应用智能故障诊断方法时会导致小故障样本问题。用小样本训练的深度神经网络不能完全训练，容易过拟合，导致实际应用性能不佳。为了解决这个问题，本文提出了一种增强了多尺度特征提取的紧凑型卷积神经网络。引入多尺度特征提取单元，在不增加卷积层的情况下提取不同时间尺度的特征，在保证分类能力的同时降低网络深度，缓解网络过于复杂带来的过拟合问题。除了，一个专门设计的紧凑型卷积神经网络综合分析多尺度特征。通过结合这两个技巧，所提出的神经网络可以以相对较浅的结构提取更敏感的特征，从而提高小样本下的分类精度。Dropout 技术也用于防止网络过拟合。三个轴承数据集验证了所提出方法的有效性。实验表明，即使在不同的负载和混合转速下，该网络也可以在有限的训练样本下获得有竞争力的结果。这提高了小样本下的分类精度。Dropout 技术也用于防止网络过拟合。三个轴承数据集验证了所提出方法的有效性。实验表明，即使在不同的负载和混合转速下，该网络也可以在有限的训练样本下获得有竞争力的结果。这提高了小样本下的分类精度。Dropout 技术也用于防止网络过拟合。三个轴承数据集验证了所提出方法的有效性。实验表明，即使在不同的负载和混合转速下，该网络也可以在有限的训练样本下获得有竞争力的结果。