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 t...Show MoreMetadata
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 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.
Published in: IEEE Transactions on Instrumentation and Measurement ( Volume: 72)