Small-sample fault diagnosis study of rolling bearings based on a residual parameterised convolutional capsule network

Although intelligent fault diagnosis has achieved good results, the application in practical engineering scenarios is still unsatisfactory due to the lack of sufficient fault signals to support the training of the diagnosis methods and the difficulty of extracting sensitive fault features from the original signals. To address the problem that small-sample fault data limit the diagnostic performance of traditional neural networks, a multi-scale residual parametric convolutional capsule network (MRCCCN) for small-sample bearing fault diagnosis is proposed. In the MRCCCN, the input fault information is averaged and segmented multiple times and then the initial features of the multi-segmented input are extracted by residual parameterised convolution. Then, the multi-branch features are fused and fed into an improved parametric capsule network to further extract fault features and store feature information using dynamic routing. The performance of the MRCCCN is validated using the Case Western Reserve University (CWRU) rolling bearing dataset and the Paderborn University rolling bearing dataset of vibration signals and compared with some advanced deep learning methods. The comparison results show that the proposed MRCCCN is able to accurately diagnose faults under small-sample conditions and still has significant diagnostic performance in small-sample variable noise tests.