A hybrid network TEdgeNeXt for data-limited and resource-constrained fault diagnosis

Abstract

In the field of intelligent machinery fault diagnosis, overcoming challenges arising from scarce labeled data and the demand for deployment on resource-constrained edge devices is imperative. To address these hurdles, this work aims to improve the ability of deep learning models to learn strong feature representations from limited data, while also reducing the model complexity. We presenting a novel network named TEdgeNeXt, the approach begins with a new signal-to-image conversion method, which is proved to be able to acquire less training data quantity. Structurally, the Convolutional (Conv.) Encoder initially is employed with depth-wise separable convolution to control the size of model rather than the traditional convolution, and the Split Depth-wise Transpose Attention (SDTA) encoder is consequently utilized by leveraging a multidimensional processing approach and the Multi-head Self-Attention which is across the channel dimensions instead of the spatial channel. By doing so, it effectively handles challenges such as high multiply-additions (MAdds) and increased latency through Flops and params. On the other hand, the fine-tune-based transfer learning technique is able to be extended in our approach for improving the capacity of generalizing. Ultimately, it indicates the noticeable improvements in Top-1 Accuracy (T1A), Mean Precision (MP), Mean Recall (MR), and Mean F1 score (MF1) across three distinct datasets.