走向协商一致的代表性:用于双异质联合故障诊断的模型不可知知识提取
Jiaye Wang1, Pengyu Song1, Chunhui Zhao1
1College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China.
概括
联合故障诊断面临异质模型和表示偏差的挑战. 联合模型-不可知知识提取 (FedMAKE) 使用新型知识载体来提高工业环境中的准确性.
科学领域:
- 工业物联网和云端计算
- 机器学习和人工智能的人工智能
- 工艺工程和故障检测的过程工程.
背景情况:
- 工业云边缘协作中的联合故障诊断通常假设同质的客户端模型.
- 模型异质性源于个性化的模型要求,对传统的联合学习构成挑战.
- 现有的方法往往忽略了表示偏差,特别是在非相同分布的数据中.
研究的目的:
- 在联合故障诊断中解决表示偏差和模型异质性.
- 提出一种新的联合学习框架,即联合模型-不可知知识提取 (FedMAKE).
- 在工业云边缘场景中提高故障诊断的准确性和稳定性.
主要方法:
- 开发了基于过程变量重要性的两个新的,建筑独立的知识载体.
- 介绍了一种双向蒸算法,用于生成网络和客户端模型之间的知识传输.
- 为客户使用全球知识载体制定了当地目标,以指导本地提取并限制漂移.
主要成果:
- 在不需要公开数据集的情况下,FedMAKE有效地弥合了客户之间的信息差距.
- 拟议的方法产生无偏见和均衡的故障数据跨类别.
- 对 TE 和 CWRU 数据集的实验表明,FedMAKE 的表现优于基线方法,精度提高了 11.7% 和 3.31%.
结论:
- 在联邦故障诊断中,FedMAKE成功地解决了表示偏差和模型异质性的问题.
- 建筑不可知性的知识载体和双向蒸是其有效性的关键.
- 这种方法为复杂的工业环境中准确的故障诊断提供了强大的解决方案.
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