A Current-Based Fault Diagnosis Method for Rotating Machinery With Limited Training Samples
Xu H, Fujia Du, Kang Huang, Jize Qiu, Xin Zhong
Abstract
The current-based fault diagnosis method is a feasible 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 paper proposes a novel lightweight fault diagnosis method based on Convolutional Neural Network (CNN), called CombFilterNet (CF-Net). The first convolutional layer of CF-Net is called 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.