The current-based fault diagnosis method is a feasi ble way to replace the conventional vibration-based method,as it is more economical,implemental,and reliable.With the deep learning(DL)method applied,the current-based methods have achieved satisfactory diagnosis accuracy.DL methods,however, demand large quantities of training samples,which are difficult to implement in real industrial sites.To tackle this problem,this article proposes a novel lightweight fault diagnosis method based on a convolutional neural network(CNN),called CombFilterNet (CF-Net).The first convolutional layer of CF-Net is called comb filter layer(CF-layer),where the convolution kernel is the comb filter kernel(CF-kernel).Each CF-kernel only has three parameters to be updated,achieving a lightweight design that makes CF-Net suitable for limited training sample conditions. The effectiveness and generalization ability of the proposed method are validated by a laboratory-acquired current dataset and an open-source vibration dataset.The results demonstrate that the proposed method is superior to the comparative methods under limited training sample conditions.