基于变压器的条件生成转移学习网络,用于在有限数据的情况下进行跨域故障诊断
Xiang Li1, Jun Ma2,3, Jiande Wu4
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China.
Scientific reports
|February 25, 2025
概括
本研究介绍了一个基于变压器的条件生成对抗网络 (TCGAN),以提高故障诊断的数据生成质量和多样性. 新方法通过更高质量的生成数据提高了跨领域故障诊断性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 当前的生成对抗网络 (GAN) 难以平衡数据质量和多样性,特别是在有限的数据和不同的条件下.
- 这种限制阻碍了有效的故障诊断,特别是在跨领域的场景中.
研究的目的:
- 提出一个新的基于变压器的条件GAN转移学习网络 (TCGAN),以解决现有GAN的局限性.
- 提高生成数据的质量和多样性,以改善故障诊断.
主要方法:
- 开发了一个基于变压器的条件GAN (TCGAN) 生成网络,包含样本标签信息.
- 建立了一个基于TCGAN的转移学习网络.
- 实施了一种协作式的"代转移"培训策略,使用期望最大化进行并行参数更新.
主要成果:
- 与TTS-GAN和CorGAN相比,提议的TCGAN方法显示出优越的数据生成质量.
- 在来自CWRU和KUST-SY的轴承数据集上验证了有效性.
- 获得更高质量的生成数据,优于现有方法.
结论:
- TCGAN方法在生成高质量和多样化的故障诊断数据方面提供了显著的改进.
- 为提高跨领域故障诊断性能提供了一种新有效的解决方案.
- 解决了在具有挑战性的工业环境中对可靠数据生成的关键需求.
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