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半监督告知器用于工业机器人的复合故障诊断

Chuanhua Deng¹, Junjie Song¹, Chong Chen¹

¹Guangdong Provincial Key Laboratory of Cyber-Physical System, Guangdong University of Technology, Guangzhou 510006, China.

Sensors (Basel, Switzerland)

|June 27, 2024

概括

本研究介绍了一种半监督的Informer算法用于工业机器人故障诊断,有效地使用有限的标记数据与丰富的未标记数据来获得准确的结果.

科学领域:

机器人技术机器人技术机器人技术
人工智能的人工智能
机器学习机器学习

背景情况:

工业机器人部署需要精确的故障诊断,以提高运营效率.
当前的智能诊断方法通常需要大量的标记数据,这在现实世界的工业环境中很少.
在线监控产生大量的未标记数据与有限的标记样本一起.

研究的目的:

开发一个有效的故障诊断模型,用于工业机器人使用半监督学习.
为了应对工业机器人故障诊断中有限的标记数据的挑战.
利用Informer模型的功能来提高诊断准确度.

主要方法:

开发了一种半监督的Informer算法,将Informer模型的内存特征与半监督的学习原理结合起来.
该算法旨在处理标记和未标记的在线监控数据.
该模型的长期和短期记忆能力被用于全面的数据分析.

主要成果:

拟议的半监督的Informer算法在故障诊断中表现出高准确度.
通过有效地利用有限的标签数据与大量的未标签数据一起实现了这一目标.
该模型证明,即使使用最小的标记样本,也能够准确诊断故障.

结论:

半监督的Informer算法为工业机器人故障诊断提供了一个可行的解决方案,使用有限的标记数据.
这种方法提高了制造环境中故障检测的可靠性和效率.
该研究验证了将先进的人工智能模型与半监督学习相结合的有效性,用于复杂的工业应用.

关键词:

深度学习是一种深度学习.错误诊断错误诊断错误诊断是一个问题.工业机器人工业机器人工业机器人半监督学习半监督学习

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