A Lightweight Transformer With Strong Robustness Application in Portable Bearing Fault Diagnosis
Hairui Fang, Jialin An, Han Liu, Jiawei Xiang, Bo Zhao, Fir Dunkin
Abstract
Although Transformer has 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, that is, 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 X-self-attention convolution neural network (XACNN), to meet these challenges. Through the adjustment of FFT, the effectiveness of the preprocessed signal is improved to enhance 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 smartphone as a portable fault diagnosis device, which verified its feasibility. As the first attempt to build a portable mobile detection device based on the deep-learning model, this article provides a new detection scheme for the relevant practitioners of mechanical fault diagnosis.