For the application of deep learning in the field of fault diagnosis, its recognition accuracy is limited by the size and quality of the training samples, such as small size samples, low signal-to-noise ratio and different working conditions. In order to solve above problems, one novel method for fault classification is proposed based on a Bidirectional Long Short-Term Memory (Bi-LSTM) and a Capsule Network with convolutional neural network (BLC-CNN). The Bi-LSTM is utilized to achieve the feature denoising and fusion, which is extracted by CNN. The fault diagnosis with insufficient training samples is carried out by the capsule network. The influence of sample size on the method is discussed emphatically. The effectiveness and superiority of the proposed method are validated through analyzing the data of bearings and gears under different working conditions with different noise. The results indicate that the proposed method has good performance and immunity to noise.

对于深度学习在故障诊断领域的应用，其识别精度受到训练样本的大小和质量的限制，例如小样本，低信噪比和不同的工作条件。为了解决上述问题，提出了一种基于双向长短期记忆（Bi-LSTM）和带卷积神经网络的胶囊网络（BLC-CNN）的故障分类新方法。Bi-LSTM用于实现特征降噪和融合，由CNN提取。训练网络不足的故障诊断是通过胶囊网络进行的。着重讨论了样本量对方法的影响。通过分析不同噪声条件下不同工况下的轴承和齿轮数据，验证了所提方法的有效性和优越性。结果表明，该方法具有良好的性能和抗噪声能力。