基于格拉姆角场与并行卷积Transformer的列车传动系统故障诊断方法

doi:10.3901/JME.2025.24.168

摘要/Abstract

摘要： 传动系统的稳定性和可靠性对列车的安全运行至关重要。然而，现有的故障诊断方法在处理非平稳信号和捕捉复杂特征时存在局限性。为克服这些局限，提出一种基于格拉姆角场(Gram angular field，GAF)和并行卷积Transformer的列车牵引传动系统故障诊断方法。采用GAF生成时频图像，以捕获时间序列的全局信息和非线性特征。进一步地，通过并行卷积处理的方式分别对格拉姆和场及格拉姆差场图进行采样，从不同的维度丰富故障信息，有效提升模型的特征提取能力和计算效率。此外，在模型后端引入Transformer Encoder结构，与卷积神经网络结合使用，以增强对复杂时频特征的表达和分析能力。试验验证表明，所提出的方法在列车传动系统故障诊断方面具有较高的准确性和效率。

关键词: 牵引传动系统, 故障诊断, 格拉姆角场, Transformer, 卷积神经网络

Abstract: The stability and reliability of the transmission system is critical to the safe operation of trains. However, existing fault diagnosis methods have limitations in handling non-stationary signals and capturing complex features. To overcome these limitations, a train traction driveline fault diagnosis method based on Gram angular field(GAF) and parallel convolutional Transformer is proposed. The GAF is used to generate time-frequency images to capture the global information and nonlinear features of the time series. Further, the Gram sum field and Gram difference field maps are sampled by means of parallel convolutional processing, respectively, to enrich the fault information from different dimensions and effectively improve the feature extraction capability and computational efficiency of the model. In addition, the Transformer Encoder structure is introduced at the back-end of the model, which is used in conjunction with the convolutional neural network to enhance the expression and analysis capability of complex time-frequency features. Experimental validation shows that the proposed method has high accuracy and efficiency in train transmission system fault diagnosis.

Key words: traction drive system, fault diagnosis, gram angle field, Transformer, convolutional neural network

中图分类号:

U279

门志辉, 宫岛, 周劲松, 周凯. 基于格拉姆角场与并行卷积Transformer的列车传动系统故障诊断方法[J]. 机械工程学报, 2025, 61(24): 168-179.

MEN Zhihui, GONG Dao, ZHOU Jinsong, ZHOU Kai. Fault Diagnosis Method for Train Traction Transmission System Based on Gram Angle Field and Parallel Convolutional Transformer[J]. Journal of Mechanical Engineering, 2025, 61(24): 168-179.

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