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A Diffusion-Based Data Augmentation Framework for Few-Shot Fault Diagnosis of Intelligent High-Speed Train

Jianjun Xu¹, Qingbin Tong^1,2, Ruize Zhu¹

¹School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China.

Sensors (Basel, Switzerland)

|May 27, 2026

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This summary is machine-generated.

This study introduces MR-DDIM, a novel framework for generating realistic fault vibration signals to improve few-shot fault diagnosis in high-speed trains. The method enhances data augmentation for better component reliability.

Area of Science:

Engineering
Artificial Intelligence
Signal Processing

Background:

Few-shot fault diagnosis for intelligent high-speed trains is hindered by scarce and imbalanced fault data.
Existing methods struggle with generating high-fidelity vibration signals for limited fault samples.

Purpose of the Study:

To propose MR-DDIM, a class-conditional diffusion-based data augmentation framework.
To generate high-fidelity fault vibration signals from limited labeled data for improved fault diagnosis.

Main Methods:

Developed a WT-UNet denoising backbone integrating 1D wavelet convolution and Feature-Wise Linear Modulation (FiLM).
Incorporated log-σ regularization and multi-resolution STFT consistency loss for training stability and spectral fidelity.
Introduced multi-resolution spectral correlation coefficient (MR-SCC) and class-intrinsic maximum mean discrepancy (cMMD) for quality evaluation.

Keywords:

diffusion model fault diagnosis high-speed train imbalanced samples

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Main Results:

MR-DDIM successfully generated fault samples with high spectral consistency and intra-class diversity.
The generated data significantly improved the robustness of downstream few-shot fault diagnosis models.
Experimental validation on BJTU-RAO datasets confirmed the method's effectiveness.

Conclusions:

MR-DDIM offers an effective data augmentation solution for intelligent fault diagnosis in high-speed railway systems.
The framework addresses the challenge of limited and imbalanced fault data in critical infrastructure monitoring.