基于数字双胞胎和域调整的跨式单轨列车变速箱故障诊断
Ling Zhao1, Jiaji Qin1, Hang Wang1
1School of Information Science and Engineering, Chongqing Jiaotong University, Xuefu Avenue No. 66, Nan'an District, Chongqing City, China.
ISA transactions
|November 11, 2025
概括
本研究引入了一种用于机械故障诊断的数字双胞胎辅助方法,即使使用未知的目标域数据,也可以准确识别变速箱问题. 该方法增强了工业机械的跨领域转移学习.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 跨领域的机械故障诊断旨在将知识从来源转移到目标领域.
- 现有的方法往往需要从目标域获得广泛的标记数据,这在工业环境中是不切实际的.
- 目标域中缺乏标记数据,阻碍了有效的故障诊断传输.
研究的目的:
- 提出使用数字双胞胎辅助的通用域适应性故障诊断方法.
- 为应对机械系统目标领域中未知故障信息的挑战.
- 提高故障诊断在现实工业场景中的准确性和适用性.
主要方法:
- 使用数字双胞胎技术构建了一个跨座单轨列车变速箱的物理模拟模型.
- 通过模拟数据增加培训数据集,并使用剩余收缩网络进行知识蒸.
- 使用Mixup数据增强和多分类器来识别目标域中的已知/未知信息.
主要成果:
- 拟议的数字双胞胎辅助方法显示了高诊断性能.
- 即使在目标域中的设备状态未知时,也可以实现有效的知识传输.
- 与实验平台数据的验证证实了该方法的稳定性.
结论:
- 数字双胞胎辅助域自适应故障诊断方法在实际的工业应用中是有效的.
- 该方法成功地克服了在目标域中要求标记数据的局限性.
- 这项工作推进了机械故障诊断领域,通过在数据稀缺的情况下实现强大的性能.
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