用频域增强的深度学习模型，一次诊断列车传动系统多重故障。
Compound Fault Diagnosis for Train Transmission Systems Using Deep Learning with Fourier-enhanced Representation
- 引入频域表示法，捕捉多部件耦合振动特征
- 单故障准确率97.67%，复合故障准确率93.93%
- 适用于真实工况下多部件协同故障诊断
故障诊断可保障列车传动系统的稳定可靠，防止运行中断。数据驱动模型在处理非线性、自适应、可扩展性和自动化方面优于传统方法。然而，现有模型通常针对单一部件独立训练，且仅考虑单故障，受限于现有数据集。当多个部件协同工作时，其振动信号相互影响，导致现有模型性能下降。为此，本文提出一种频域表示方法与一维卷积神经网络，用于复合故障诊断，并在包含21个传感器通道、17种单故障和42种复合故障的PHM Beijing 2024数据集上进行验证。该数据集涵盖电机、齿轮箱、左轴箱和右轴箱四个相互作用部件。所提模型在单故障测试集上达到97.67%准确率，在复合故障测试集上达到93.93%准确率。
原文摘要 · Abstract (English)
Fault diagnosis prevents train disruptions by ensuring the stability and reliability of their transmission systems. Data-driven fault diagnosis models have several advantages over traditional methods in terms of dealing with non-linearity, adaptability, scalability, and automation. However, existing data-driven models are trained on separate transmission components and only consider single faults due to the limitations of existing datasets. These models will perform worse in scenarios where components operate with each other at the same time, affecting each component's vibration signals. To address some of these challenges, we propose a frequency domain representation and a 1-dimensional convolutional neural network for compound fault diagnosis and applied it on the PHM Beijing 2024 dataset, which includes 21 sensor channels, 17 single faults, and 42 compound faults from 4 interacting components, that is, motor, gearbox, left axle box, and right axle box. Our proposed model achieved 97.67% and 93.93% accuracies on the test set with 17 single faults and on the test set with 42 compound faults, respectively.
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