基于终身学习的分数顺序卷积编码模型,用于在多种条件下对电源转换器的开放电路故障诊断
Tao Li1,2,3, Enyu Wang4, Jun Yang3
1College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
本研究介绍了一种终身学习模型,用于诊断功率转换器的开放电路故障. 这种新的方法在各种操作条件下提高了精度,确保了系统的稳定性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 开放电路 (OC) 故障在功率转换器中至关重要,影响电机驱动系统的稳定性.
- 现有的诊断模型难以应对不断变化的操作条件,导致准确性降低.
研究的目的:
- 开发一种先进的故障诊断模型,用于不同操作条件下的功率转换器.
- 在动态环境中提高故障检测的稳定性和准确性.
主要方法:
- 提出了一种支持终身学习的分数顺序卷积编码器模型.
- 使用卷积和编码器模块进行自动特征提取和识别.
- 实施了多层次的终身学习框架,以防止灾难性的遗忘.
主要成果:
- 在多种条件下,在85个故障类别中实现了96.89%的诊断准确度.
- 在不同的操作参数下,证明了故障特征的有效学习.
- 成功地应对了随着条件的变化而降低准确度的挑战.
结论:
- 拟议的模型显著提高了电源转换器开放电路故障诊断的准确性.
- 终身学习和分数顺序优化提高了模型的适应性和长期依赖性捕获.
- 该框架为动态系统中持续故障监控提供了强大的解决方案.
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