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Design and Analysis for Fall Detection System Simplification
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已知类意识到和新出现的未知类拒绝基于对抗性培训,用于开放式设置故障诊断
Bo She1, Weige Liang1, Fenqi Qin2
1Department of Weaponry Engineering, Naval University of Engineering, Wuhan 430000, China.
ISA transactions
|July 15, 2023
概括
这项研究引入了一种用于故障诊断领域适应的新方法,有效地处理新的故障状态. 该方法对齐已知类并拒绝未知类,提高了现实世界的诊断准确性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 错误诊断 错误诊断 错误诊断 错误诊断 错误诊断 错误诊断
背景情况:
- 传统的域名适应方法假设相同的标签空间,在新的故障状态下失败.
- 由于源域和目标域之间的标签空间部分重叠,开放式域的适应具有挑战性.
研究的目的:
- 提出一种在故障诊断中进行开放式域调整的方法,可以处理新出现的未知故障状态.
- 在现实应用中提高诊断系统的准确性和稳定性.
主要方法:
- 引入了一个基于的自适应加权学习方案,用于最大分类器差异方法.
- 使用交互式对抗性培训来提取域不变特征.
- 开发了二进制交叉方案和模块,以区分已知和未知类.
- 建立了拒绝未知目标类的综合标准.
主要成果:
- 在三个机器数据集上表现出卓越的性能.
- 有效地将目标已知类型样本与来源已知类型样本对齐.
- 在特征对齐过程中成功抑制未知类型样本的影响.
结论:
- 提出的已知类意识和新兴未知类拒绝 (KAEUR) 方法有效地解决了故障诊断中的开放式域调整.
- 凯尔通过区分已知和新出现的故障状态来提高诊断准确度.
- 该方法显示了对现实世界工业应用的巨大潜力,需要强大的故障检测.
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