基于网络物理系统的汽车零部件智能生产线状态监测技术研究

Yifei Wang, Zhiwen Xia, Kexin Yang, Lijun Jin
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引用次数: 0

摘要

生产线信息物理系统(CPS)是实现制造业智能化转型的重要技术支撑。因此,本文分析了CPS在生产线中的应用,分析了其在生产线中的建模方法;在此基础上,研究了基于信号处理和深度学习算法的生产线状态信号分析技术,提高了生产线状态监测的质量。在上述分析的基础上,本文构建了基于CPS混合建模的汽车零部件生产线状态监测系统框架,克服了传统监测系统的不足,提高了系统的分析决策能力;为了检验框架的有效性,本文以汽车零部件智能生产线中的主轴和轴承数据为例,对比了相关算法,构建了基于CPS框架的监控系统,测试了CPS框架在智能生产线状态监控中的有效性,证明了该框架在智能生产线中的可推广性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Condition Monitoring Technology of Automobile Parts Intelligent Production Line Based on Cyber Physical System
Cyber physical system (CPS) of production line is an important technical support to realize the intelligent transformation of manufacturing industry. Therefore, this paper analyzes the application of CPS in the production line, and analyzes its modeling method in the production line; on this basis, the production line state signal analysis technology based on signal processing and deep learning algorithm is studied, which improves the quality of production line state monitoring. Based on the above analysis, this paper constructs the condition monitoring system framework of automobile parts production line based on CPS hybrid modeling, which overcomes the shortcomings of the traditional monitoring system and improves the analysis and decision-making ability of the system; In order to test the effectiveness of the framework, taking the spindle and bearing data in the automobile parts intelligent production line as an example, this paper compares the relevant algorithms, constructs a monitoring system based on the CPS framework, tests the effectiveness of the CPS framework in the condition monitoring of the intelligent production line, and proves that the framework can be popularized in the intelligent production line.
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