RS-SCBiGRU:一个耐噪声的神经网络,用于高速电机故障诊断,采用有限的样本
Sun Fenghao1,2, Li Guofa3,4, He Jialong1,2
1Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, Changchun, 130025, China.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种先进的方法,用于使用有限的数据来诊断高速电机故障. 新型号RS-SCBiGRU在机械故障诊断中提高了准确性和抗噪能力.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 卷积神经网络 (CNN) 对机械故障诊断至关重要,但需要大量的数据.
- 现实世界的场景往往缺乏足够的故障数据,阻碍了传统方法.
研究的目的:
- 在小样本情况下为高速电机开发故障诊断方法.
- 用有限的数据提高故障检测的准确性和稳定性.
主要方法:
- 设计了一种创新的数据增强技术,以扩大训练数据的多样性.
- 提出了一种新的频率适应卷积层 (SCNET),以改善特征提取.
- 该SCNET层与双向门式循环单元 (BiGRU) 集成,以创建RS-SCBiGRU模型.
主要成果:
- 该RS-SCBiGRU模型显示了与现有方法相比显著的准确性改进.
- 拟议的模型在故障诊断中表现出对噪声的增强抵抗力.
- 综合数据增强和SCNET层有效地解决了小样本的局限性.
结论:
- 在数据稀缺的情况下,RS-SCBiGRU模型为高速电机故障诊断提供了高效和有效的解决方案.
- 频率适应方法和数据增强是改善诊断性能的关键.
- 这项研究在具有挑战性的工业环境中推进了智能故障诊断.
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