数字双胞胎辅助自主监督对比学习:一种用于电机设备故障诊断的新框架
Jiawei Lu1, Chao Lu1, Qibing Wang1
1China Jiliang University, Hangzhou, China.
ISA transactions
|December 25, 2025
概括
本研究介绍了用于电机设备故障诊断的数字双胞胎辅助自主监督学习框架. 该方法增强了数据稀缺性,并在各种操作条件下提高了诊断准确性.
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:
- 机电设备对于工业运作至关重要.
- 错误诊断面临着诸如有限的数据和糟糕的概括等挑战.
- 现有的方法与现实世界的工业限制作斗争.
研究的目的:
- 为机电设备故障诊断提出一个新的框架.
- 在工业环境中解决数据稀缺和泛化问题.
- 为了提高故障检测的准确性和稳定性.
主要方法:
- 为数据模拟构建了一个五维数字双胞胎模型 (DTEE).
- 雇员数据增强和自我监督的时间频率对比学习 (DT-SSTFCL).
- 使用双流变压器编码器和交叉相关损失矩阵 (CCLM) 进行特征学习.
主要成果:
- DTEE模型生成模拟数据以克服缺陷数据不足和类不平衡.
- DT-SSTFCL有效地学习了互补的时间和频率域特征.
- 调整波纹内核 (SWK) 的优化增强了时间频率特征的理解.
结论:
- 拟议的框架显著优于现有的故障诊断方法.
- 它提供了一种可行的解决方案,用于电机械设备故障诊断,使用有限的标记数据.
- 该方法在不同的操作场景中展示了强大的概括能力.
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