智能制造中的多传感器数据融合技术研究
摘要
智能制造作为推动工业4.0发展的核心驱动力，对多传感器数据融合技术提出了更高要求，以实现生产过程的高效、精准和智能化管理。本研究旨在探索多传感器数据融合技术在智能制造中的应用潜力及其优化策略，通过构建基于深度学习与贝叶斯网络的融合算法框架，解决传统方法在复杂工业环境下的局限性。研究采用多层次数据处理模型，结合时间序列分析与特征提取技术，有效提升了数据融合的精度与鲁棒性。实验结果表明，所提出的融合算法在噪声抑制、异常检测及动态场景适应能力方面具有显著优势，能够显著提高智能制造系统的决策效率与可靠性。本研究的主要创新点在于提出了一种自适应权重调整机制，可根据实时工况动态优化传感器数据的融合比例，从而更好地满足多样化工业需求。此外，研究还验证了该技术在智能装配、质量监控等典型应用场景中的可行性与优越性，为未来智能制造系统的设计与优化提供了重要参考。
关键词：智能制造；多传感器数据融合；深度学习；贝叶斯网络
Abstract
Smart manufacturing, as the core driving force behind the development of Industry 4.0, imposes higher demands on multi-sensor data fusion technology to achieve efficient, precise, and intelligent management of production processes. This study aims to explore the application potential and optimization strategies of multi-sensor data fusion technology in smart manufacturing by constructing a fusion algorithm fr amework based on deep learning and Bayesian networks, addressing the limitations of traditional methods in complex industrial environments. The research employs a multi-level data processing model combined with time series analysis and feature extraction techniques, effectively enhancing the accuracy and robustness of data fusion. Experimental results demonstrate that the proposed fusion algorithm exhibits significant advantages in noise suppression, anomaly detection, and adaptability to dynamic scenarios, thereby substantially improving the decision-making efficiency and reliability of smart manufacturing systems. A key innovation of this study is the introduction of an adaptive weight adjustment mechanism that can dynamically optimize the fusion ratio of sensor data according to real-time working conditions, better meeting diverse industrial needs. Furthermore, the feasibility and superiority of this technology have been validated in typical application scenarios such as intelligent assembly and quality monitoring, providing important references for the design and optimization of future smart manufacturing systems.
Keywords：Intelligent Manufacturing; Multi-Sensor Data Fusion; Deep Learning; Bayesian Network
目 录
摘要 I
Abstract II
一、绪论 1
(一) 智能制造与多传感器数据融合背景 1
(二) 多传感器数据融合技术的研究现状 1
(三) 本文研究方法与技术路线 2
二、多传感器数据融合的基础理论 2
(一) 数据融合的基本概念与分类 2
(二) 常用数据融合算法概述 3
(三) 数据融合在智能制造中的关键需求 4
三、多传感器数据融合的关键技术分析 4
(一) 传感器数据预处理技术 4
(二) 融合算法的优化与选择 5
(三) 数据一致性与校准技术 6
四、多传感器数据融合的应用实践与案例研究 6
(一) 智能制造中典型应用场景分析 6
(二) 数据融合技术的实际应用案例 7
(三) 应用效果评估与改进建议 8
结 论 9
参考文献 10
摘要
智能制造作为推动工业4.0发展的核心驱动力，对多传感器数据融合技术提出了更高要求，以实现生产过程的高效、精准和智能化管理。本研究旨在探索多传感器数据融合技术在智能制造中的应用潜力及其优化策略，通过构建基于深度学习与贝叶斯网络的融合算法框架，解决传统方法在复杂工业环境下的局限性。研究采用多层次数据处理模型，结合时间序列分析与特征提取技术，有效提升了数据融合的精度与鲁棒性。实验结果表明，所提出的融合算法在噪声抑制、异常检测及动态场景适应能力方面具有显著优势，能够显著提高智能制造系统的决策效率与可靠性。本研究的主要创新点在于提出了一种自适应权重调整机制，可根据实时工况动态优化传感器数据的融合比例，从而更好地满足多样化工业需求。此外，研究还验证了该技术在智能装配、质量监控等典型应用场景中的可行性与优越性，为未来智能制造系统的设计与优化提供了重要参考。
关键词：智能制造；多传感器数据融合；深度学习；贝叶斯网络
Abstract
Smart manufacturing, as the core driving force behind the development of Industry 4.0, imposes higher demands on multi-sensor data fusion technology to achieve efficient, precise, and intelligent management of production processes. This study aims to explore the application potential and optimization strategies of multi-sensor data fusion technology in smart manufacturing by constructing a fusion algorithm fr amework based on deep learning and Bayesian networks, addressing the limitations of traditional methods in complex industrial environments. The research employs a multi-level data processing model combined with time series analysis and feature extraction techniques, effectively enhancing the accuracy and robustness of data fusion. Experimental results demonstrate that the proposed fusion algorithm exhibits significant advantages in noise suppression, anomaly detection, and adaptability to dynamic scenarios, thereby substantially improving the decision-making efficiency and reliability of smart manufacturing systems. A key innovation of this study is the introduction of an adaptive weight adjustment mechanism that can dynamically optimize the fusion ratio of sensor data according to real-time working conditions, better meeting diverse industrial needs. Furthermore, the feasibility and superiority of this technology have been validated in typical application scenarios such as intelligent assembly and quality monitoring, providing important references for the design and optimization of future smart manufacturing systems.
Keywords：Intelligent Manufacturing; Multi-Sensor Data Fusion; Deep Learning; Bayesian Network
目 录
摘要 I
Abstract II
一、绪论 1
(一) 智能制造与多传感器数据融合背景 1
(二) 多传感器数据融合技术的研究现状 1
(三) 本文研究方法与技术路线 2
二、多传感器数据融合的基础理论 2
(一) 数据融合的基本概念与分类 2
(二) 常用数据融合算法概述 3
(三) 数据融合在智能制造中的关键需求 4
三、多传感器数据融合的关键技术分析 4
(一) 传感器数据预处理技术 4
(二) 融合算法的优化与选择 5
(三) 数据一致性与校准技术 6
四、多传感器数据融合的应用实践与案例研究 6
(一) 智能制造中典型应用场景分析 6
(二) 数据融合技术的实际应用案例 7
(三) 应用效果评估与改进建议 8
结 论 9
参考文献 10
