一种轻量化尺度感知调制Swin Transformer模型的轴箱轴承故障诊断方法

A Lightweight Scale-Aware Modulation Swin Transformer Network for Axlebox Bearing Fault Diagnosis

针对当前Transformer网络模型运算效率偏低,且难以用于复杂工况条件下高速列车轴箱轴承故障诊断的问题,提出了一种基于时频多域融合与轻量化结构的尺度感知调制Swin Transformer(SMST)模型的轴箱轴承故障诊断方法。首先,采用格拉姆角场法、双谱法与Chirplet变换法,将轴承振动信号转化为时域、频域与时频域内的二维图像,基于多域特征融合思想集成为新的特征图像;然后,设计了一种新的轻量化结构SMST模块,在其内部实现了卷积运算与Transformer自注意力运算的进一步融合;最后,在层次化模型框架中引入特征金字塔模块(FPB),弥补不同层输出特征的不一致性,实现了上下文信息的特征深度融合及复杂工况条件下轴箱轴承故障诊断。实验结果表明:相比格拉姆角场法、双谱法、Chirplet变换法、短时傅里叶变换法、连续小波变换法等单一领域图像生成方法,时频多域融合方法生成的图像特征信息表征能力更好;所提网络模型在1010、760、505 r/min这3种转速变工况任务中的轴箱轴承故障识别准确率分别为99.88%、99.92%与99.96%;对比ResNets、GoogleNet、ViT、Swin Transformer和SMT这5种模型,所提方法的故障识别准确率更高,模型轻量化程度更好。所提方法可为实际工况中列车轴箱轴承故障诊断提供参考。

A scale-aware modulation swin transformer(SMST)model is proposed based on multi-domain fusion and lightweight structure,for axlebox bearing fault diagnosis,aiming to address the inefficiency of the current Transformer network model and its unsuitability for axlebox bearing fault diagnosis of high-speed trains under complex working conditions.Firstly,Gram angle field(GAF),bispectrum,and Chirplet transform techniques are applied to convert the bearing vibration signals into two-dimensional images in time,frequency,and time-frequency domains,which are integrated into a new image based on the idea of multi-domain feature fusion.Then,a new lightweight structure SMST module is designed,combining the further fusion of convolution operation and Transformer self-attention operation.Finally,a feature pyramid block(FPB)is introduced into the SMST hierarchical model framework to address the inconsistency of the output features across layers and enable deep feature fusion with contextual information.The fault diagnosis of axlebox bearings under complex working conditions is realized.Results show that the time-frequency multi-domain fusion method offers a superior representation of image feature information compared to single-domain image generation methods such as Gram angle field,bispectrum,Chirplet transform,short-time Fourier transform,and continuous wavelet transform.The proposed network model achieves high accuracy rates of 99.88%,99.92%,and 99.96%in identifying axlebox bearing faults in the three tasks under variable working conditions with different speeds of 1010 r/min,760 r/min,and 505 r/min,respectively.Comparative analysis against five models of ResNets,GoogleNet,ViT,Swin Transformer,and SMT showcases the higher fault identification accuracy and better model lightweight of the proposed method.The proposed method can serve as a reference for the diagnosis of axlebox bearing faults in trains under actual working conditions.