在数据异质环境中对变速箱的智能诊断基于联合监督对比学习框架
Ruoyang Bai1, Hongwei Wang2, Wenlei Sun1
1School of Mechanical Engineering, Xinjiang University, Urumqi, 830017, China.
Scientific reports
|April 26, 2025
概括
联合监督对比学习 (FSCL) 通过克服数据孤岛和异质性来增强智能故障诊断. 这种方法可以在不共享原始数据的情况下进行协作模型培训,从而改善工业设备的维护.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 工业工程 工业工程 工业工程
背景情况:
- 智能故障诊断面临由于数据孤岛和异质性导致的瓶.
- 工业环境往往缺乏故障样本,隐私问题阻碍了数据共享.
- 来自不同设备条件的数据异质性降低了联合学习的有效性.
研究的目的:
- 提出一个联邦监督对比学习 (FSCL) 框架,以解决智能故障诊断中的数据孤岛和异质性.
- 为了实现诊断模型的分布式协作培训,同时保持数据隐私.
- 为了增强全球模型特征表示,尽管数据异质.
主要方法:
- 将联合学习与受监督的对比机制相结合.
- 分布式协作培训,以克服数据孤岛.
- 一个混合的对比损失函数,以解决异质数据中的特征空间不匹配.
主要成果:
- FSCL有效地解决了故障诊断中数据不足和异质性的问题.
- 证明了全球模型的功能表示能力的改进.
- 在两个变速箱数据集上成功应用.
结论:
- FSCL为工业设备的智能维护提供了一种新的方法.
- 该框架优化了数据效率和隐私保护.
- 在分布式环境中,FSCL为协作故障诊断提供了强大的解决方案.
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