多源子域消极转移抑制和多个伪标签指导对齐:在交叉工作条件下的故障诊断方法
Xing Chen1, Hua Yin1, Qitong Chen1
1School of Mechanical and Electric Engineering, Soochow University, Suzhou 215000, China.
ISA transactions
|August 23, 2024
概括
本研究引入了一种新的方法,通过减少转移学习中的负转移来改善故障诊断. 多阶段对齐多源子域适应 (MAMSA) 方法在不同的条件下提高了诊断准确性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 工程 工程师 工程师 工程师
背景情况:
- 转移学习被广泛用于故障诊断.
- 由于分布差异,在多源域中可能会发生负转移.
- 现有的方法往往忽略了来自多个来源的子域影响.
研究的目的:
- 提出一种新的方法,即多阶段对齐多源子域适应 (MAMSA),以解决故障诊断中的负转移问题.
- 通过在多个源域中对准子域来提高故障诊断的准确性.
- 在跨领域故障诊断场景中有效地抑制负转移.
主要方法:
- 一个全局特征提取器,用于域不变特征.
- 三个特定领域的特征提取器,具有对抗式学习和分布对齐.
- 多个伪标签指导的局部最大平均差异 (MP-LMMD) 用于子域对齐.
主要成果:
- 拟议的MAMSA方法有效地抑制了负面转移.
- 在交叉工作条件下观察到诊断性能显著改善.
- 该方法在处理子域分布差异方面表现出强大的能力.
结论:
- MAMSA为错误诊断的多源转移学习中的负转移问题提供了一个有希望的解决方案.
- 该方法提高了诊断的准确性和可靠性,特别是在具有挑战性的跨领域场景中.
- 这项研究有助于推进智能故障诊断系统领域的发展.
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