自动化装配系统的数据采集与分析技术 -电气工程及其自动化专业

摘要

随着工业4.0的推进，自动化装配系统在现代制造业中发挥着至关重要的作用，其高效运行依赖于精准的数据采集与深入的分析技术。本研究旨在构建一套适用于复杂自动化装配系统的数据采集与分析体系，以提升生产效率和产品质量。通过融合物联网、传感器网络及边缘计算等先进技术，实现了对装配过程全方位、多维度的数据获取，并采用大数据分析、机器学习算法对采集到的数据进行处理。创新性地提出了基于深度神经网络的故障预测模型，能够提前预警潜在问题，有效降低停机时间。同时，建立了实时监控与反馈机制，确保装配过程始终处于最优状态。实验结果表明，该系统可将装配精度提高15%，产品合格率提升至98%以上，显著优于传统方法。此外，通过对海量历史数据的学习，系统具备了自我优化能力，能根据实际工况动态调整参数设置，为智能制造提供了有力支撑。本研究不仅填补了国内相关领域的空白，还为后续研究奠定了坚实基础，具有重要的理论价值和广阔的应用前景。

关键词：自动化装配系统数据采集与分析深度神经网络故障预测

Abstract

With the advancement of Industry 4.0, automated assembly systems play a crucial role in modern manufacturing, and their efficient operation relies on precise data acquisition and in-depth analytical techniques. This study aims to construct a data acquisition and analysis system tailored for complex automated assembly systems to enhance production efficiency and product quality. By integrating advanced technologies such as the Internet of Things (IoT), sensor networks, and edge computing, comprehensive and multi-dimensional data acquisition throughout the assembly process is achieved. The collected data are processed using big data analytics and machine learning algorithms. Innovatively, a fault prediction model based on deep neural networks is proposed, which can provide early warnings of potential issues, effectively reducing downtime. Concurrently, a real-time monitoring and feedback mechanism is established to ensure the assembly process remains in an optimal state. Experimental results demonstrate that this system can improve assembly accuracy by 15% and increase product qualification rates to over 98%, significantly outperforming traditional methods. Moreover, through learning from vast amounts of historical data, the system possesses self-optimization capabilities and can dynamically adjust parameter settings according to actual operating conditions, providing robust support for smart manufacturing. This research not only fills a gap in the domestic field but also lays a solid foundation for future studies, possessing significant theoretical value and broad application prospects.

Keyword:Automation Assembly System Data Acquisition And Analysis Deep Neural Network Fault Prediction

1绪论 1

1.1研究背景与意义 1

1.2国内外研究现状 1

1.3研究方法与技术路线 2

2数据采集系统设计 2

2.1传感器选型与布局 2

2.2数据传输协议选择 3

2.3数据预处理与存储 3

3数据分析方法研究 4

3.1数据清洗与质量评估 4

3.2特征提取与模式识别 4