You can get smaller: A lightweight self-activation convolution unit modified by transformer for fault diagnosis

稳健性(进化) 计算机科学 卷积神经网络 变压器 人工智能 算法 可靠性工程 电子工程 工程类 电压 电气工程 生物化学 基因 化学
作者
Hairui Fang,Jin Deng,DongSheng Chen,Wenjuan Jiang,Siyu Shao,Mingcong Tang,Jingjing Liu
出处
期刊:Advanced Engineering Informatics [Elsevier BV]
卷期号:55: 101890-101890 被引量:48
标识
DOI:10.1016/j.aei.2023.101890
摘要

The fault diagnosis methods based on convolutional neural network (CNN) have achieved many excellent results. However, owing to the deployment cost, numerous CNNs with large parameters are difficult to be directly applied to industrial practice. Therefore, this work aims to use lower parameters (order of magnitude is thousand) to complete the task of bearing fault diagnosis on the premise that the model has high-accuracy. To achieve this goal, a convolution unit modified by transformer was proposed, who is based upon the self-activation function, which makes the transformer and CNN organically integrated into a whole. Then, based on this unit, a series of novel lightweight diagnosis frameworks were proposed, named SANet. Finally, it was demonstrated that the proposed SANet can complete the high-accuracy diagnosis task with less than three thousand parameters and has strong robustness to noise (Average accuracy in various noise environments: 84.55%), and that SANet can achieve satisfactory results when there are few training samples (The number of samples of each category is 3 × 4), through four research cases. To sum up, based on this novel unit, we provide a series of lightweight frameworks with high-accuracy, strong robustness, and low sample demand, which is expected to promote the process of fault diagnosis technology from theoretical research to industrial practice.
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