High precision and fast fault diagnosis is an important guarantee for the safe and reliable operation of machinery. In recent years, due to the strong recognition ability, data-driven fault diagnosis technology based on deep learning has attracted enormous attention. The fault diagnosis module proposed by many scholars has achieved excellent recognition results, but some of them are too complex to deploy in practice, due to the high costs. In this article, an efficient feature extraction method based on the convolutional neural networks (CNN) was proposed, and the high-precision fault diagnosis task was completed used a lightweight network. First, a 1-D CNN (1-D-CNN) is used to extract the multichannel features for the input original signals, to generate the feature maps which are highly consistent with the 2-D CNN (2-D-CNN). Second, a strategy of the lightweight model is proposed based on the principle of dynamic convolution and separable convolution. Third, a spatial attention mechanism (SAM) is used to adjust the weights of the output feature maps. Finally, the model completes the diagnosis task through the learned features. The performance of the model was verified under different operating conditions and noise environments. The experimental results demonstrate that the proposed method has excellent anti-noise ability and domain adaptability.

Although Transformers have achieved excellent results in various tasks in industrial scenes, owing to the environmental noise and cost limitation, the fault diagnosis approaches based on Transformer are facing two serious challenges, i.e. robustness and lightweight. With the original intention of promoting the transformation of Transformer from theoretical design to practical engineering application, we designed a lightweight framework with strong robustness, named XACNN, to meet these challenges. Through the adjustment of FFT, the effectiveness of the pre-processed signal is improved to enhance enhances the robustness to noise, and the goal of lightweight is achieved (FLOP: 0.136 M, Params: 7.663 k) via the optimization of self-attention. To demonstrate the effectiveness, the performance of XACNN in various noise environments was tested on a self-made dataset (average accuracy: 88.525%), which is superior to other methods. Simultaneously, we deployed XACNN on a smart phone as a portable fault diagnosis device, which verified the feasibility. As the first attempt to build a portable mobile detection device based on the deep learning model, this paper provides a new detection scheme for the relevant practitioners of mechanical fault diagnosis.