联合转移学习策略:基于修复数据的新型跨设备故障诊断方法
Zhenhao Yan1, Jiachen Sun1, Yixiang Zhang1
1Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究引入了用于故障诊断的联合学习方法,使用修复的数据来克服数据腐败带来的挑战. 该方法增强了模型训练,并防止了负参数传输,提高了诊断准确性和数据隐私.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 工业工程 工业工程 工业工程
背景情况:
- 联合学习 (FL) 对于故障诊断中的数据隐私至关重要.
- 有效的故障诊断需要持续的模型训练与完整的数据.
- 数据腐败给FL模型培训和参数传输带来了重大挑战.
研究的目的:
- 提出一种使用修复数据的新型跨设备故障诊断方法.
- 解决模型培训困难和负参数传输问题,这是由于FL的数据损坏造成的.
- 为了提高故障诊断性能,同时保持数据隐私.
主要方法:
- 当地模型培训包括随机森林回归来修复缺少片段的数据.
- 联合域差异损失被引入以减轻局部训练期间的参数偏差.
- 适应性更新策略用于全球模型聚合和本地模型更新.
主要成果:
- 拟议的方法有效地修复损坏的故障样本用于网络培训.
- 联合域差异损失纠正参数偏差,防止错误的故障特征.
- 适应性更新管理来自本地模型更新的性能变化.
- 轴承数据集的实验验证表明,与现有的联合转移学习方法相比,故障诊断性能和数据隐私保护优越.
结论:
- 新的联合学习方法有效地处理故障诊断中的数据损坏.
- 修复的数据和域差异损失提高了模型的准确性和稳定性.
- 适应性更新可确保工业应用的稳定和有效的联合学习.
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