| 韦宝涛,徐劲松,盛润,王博.数据生成下的航空活塞发动机进排气故障诊断[J].电子测量与仪器学报,2025,39(12):91-103 |
| 数据生成下的航空活塞发动机进排气故障诊断 |
| Fault diagnosis of intake and exhaust systems in aeropiston engine under data generation |
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| DOI： |
| 中文关键词: 航空活塞发动机 进排气堵塞故障 生成对抗网络（GAN） Vision Transformer编码器 |
| 英文关键词:aero piston engine intake and exhaust blockage faults generative adversarial network (GAN) vision transformer encoder |
| 基金项目: |
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| Author | Institution |
| Wei Baotao | Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China |
| Xu Jinsong | Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China |
| Sheng Run | Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China |
| Wang Bo | Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China |
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| 中文摘要: |
| 针对航空活塞发动机的进排气系统不同堵塞程度下的故障数据稀缺、各类样本数量不均衡导致诊断效果不佳、鲁棒性差等问题，设置了小样本故障诊断和类不均衡故障诊断两个实验场景，并提出了一种基于迁移架构的梯度惩罚类别条件生成对抗网络（TCWGAN-GP），用于生成指定类别的高质量多源故障样本。TCWGAN-GP的生成器以Vision Transformer的编码器为基础骨干网络，以充分捕捉到各分块数据源间的对应关系；在损失函数中结合Wasserstein距离和梯度惩罚项，防止模型崩溃和梯度消失，提高对抗训练的稳定性。生成的样本经筛选后与原始数据合并，用于诊断模型的训练以检验样本的质量。在两个场景下的两种稳定工况数据集实验中，平均测试准确率相较于原始数据集均有不同幅度的提升，如1 750 r/min_50%油门数据集在类不均衡实验中，训练轮次分别为30和50时，平均测试准确率分别提升了55.74%和59.26%。在消融实验中，所提方法生成的样本更接近真实样本，在诊断测试中准确率达到100%，其测试准确率和鲁棒性均优于其他生成方法。 |
| 英文摘要: |
| To address the issues of scarce fault data for varying blockage levels of intake or exhaust system in aero piston engine, and the resultant unbalanced sample sizes that lead to poor diagnostic performance and low robustness, this paper defined two experimental scenarios: fault diagnosis under small-sample conditions and fault diagnosis under class-imbalance conditions. A Transfer-Architecture-based class conditional Wasserstein GAN with gradient penalty (TCWGAN-GP) was proposed to generate high-quality multi-source fault samples of specified categories. The generator of TCWGAN-GP was based on the encoder of Vision Transformer as the backbone network to fully capture the corresponding relationships among different block data sources. The loss function combines the Wasserstein distance and the gradient penalty term GP to prevent model collapse and gradient vanishing, thereby enhancing the stability of adversarial training. The screened and generated samples were merged with the original data for training the diagnostic model to verify the quality of the samples. Experiments were conducted under two stable operating conditions across the two defined scenarios. The average test accuracy was improved to varying degrees compared to the original dataset. For example, in the class-imbalanced experiment of the 1 750 r/min_50% throttle dataset, the average test accuracy increased by 55.74% and 59.26% when the training rounds were 30 and 50, respectively. In the ablation experiment, the samples generated by the proposed method were closer to the real samples, achieving an accuracy rate of 100% in the diagnostic test, Its test accuracy and robustness were superior to other generation methods. |
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