智能制造中的设备故障预测与健康管理
摘 要
随着工业4.0的推进，智能制造已成为制造业转型升级的核心方向，而设备故障预测与健康管理（PHM）作为保障智能生产系统高效稳定运行的关键技术，受到广泛关注。本研究旨在通过融合先进的数据驱动方法与领域知识，构建一套适用于复杂制造环境的设备故障预测与健康管理系统。研究基于多源异构传感器数据，提出了一种结合深度学习与信号处理的混合模型，能够有效提取设备运行状态特征并实现精准预测。同时，引入贝叶斯网络优化不确定性分析，提升了预测结果的可靠性。实验结果表明，该方法在多种典型工业场景中表现出优异的性能，预测准确率较传统方法提升15%以上，且具备较强的泛化能力。此外，研究开发了实时监控与决策支持模块，为运维人员提供智能化建议，显著降低了设备维护成本和停机时间。本研究的主要创新点在于将深度学习的非线性建模能力与领域知识的先验优势有机结合，突破了传统方法在复杂工况下的局限性，为智能制造中的设备健康管理提供了新思路和技术支撑。
关键词：智能制造；深度学习；贝叶斯网络；多源异构数据
Abstract
With the advancement of Industry 4.0, intelligent manufacturing has become the core direction for the transformation and upgrading of the manufacturing industry, while predictive maintenance and health management (PHM) have gained significant attention as a key technology to ensure the efficient and stable operation of intelligent production systems. This study aims to construct a PHM system suitable for complex manufacturing environments by integrating advanced data-driven approaches with domain knowledge. Based on multi-source heterogeneous sensor data, a hybrid model combining deep learning and signal processing is proposed, which effectively extracts operational state features of equipment and achieves precise predictions. Meanwhile, Bayesian networks are introduced to optimize uncertainty analysis, thereby enhancing the reliability of prediction results. Experimental results demonstrate that this method exhibits superior performance in various typical industrial scenarios, with a prediction accuracy improvement of over 15% compared to traditional methods, along with strong generalization capabilities. Additionally, a real-time monitoring and decision support module has been developed, providing intelligent recommendations to maintenance personnel and significantly reducing equipment maintenance costs and downtime. The primary innovation of this research lies in the organic combination of deep learning's nonlinear modeling capabilities with the prior advantages of domain knowledge, overcoming the limitations of traditional methods under complex working conditions and offering new insights and technical support for equipment health management in intelligent manufacturing..
Key Words：Intelligent Manufacturing；Deep Learning；Bayesian Network；Multi-Source Heterogeneous Data
目 录
摘 要 I
Abstract II
第1章 绪论 2
1.1 智能制造中设备健康管理的背景与意义 2
1.2 设备故障预测与健康管理的研究现状 2
1.3 本文研究方法与技术路线 3
第2章 设备故障预测的关键技术分析 4
2.1 数据采集与预处理技术 4
2.2 故障特征提取与模式识别 4
2.3 预测模型构建与优化方法 5
第3章 健康管理策略与系统设计 6
3.1 设备健康状态评估体系 6
3.2 基于大数据的健康管理平台架构 6
3.3 实时监控与预警机制设计 7
第4章 智能制造中的应用案例与效果验证 8
4.1 典型智能制造场景分析 8
4.2 故障预测与健康管理的实际应用 8
4.3 系统性能评估与改进方向 9
结 论 9
参考文献 11
致 谢 12
摘 要
随着工业4.0的推进，智能制造已成为制造业转型升级的核心方向，而设备故障预测与健康管理（PHM）作为保障智能生产系统高效稳定运行的关键技术，受到广泛关注。本研究旨在通过融合先进的数据驱动方法与领域知识，构建一套适用于复杂制造环境的设备故障预测与健康管理系统。研究基于多源异构传感器数据，提出了一种结合深度学习与信号处理的混合模型，能够有效提取设备运行状态特征并实现精准预测。同时，引入贝叶斯网络优化不确定性分析，提升了预测结果的可靠性。实验结果表明，该方法在多种典型工业场景中表现出优异的性能，预测准确率较传统方法提升15%以上，且具备较强的泛化能力。此外，研究开发了实时监控与决策支持模块，为运维人员提供智能化建议，显著降低了设备维护成本和停机时间。本研究的主要创新点在于将深度学习的非线性建模能力与领域知识的先验优势有机结合，突破了传统方法在复杂工况下的局限性，为智能制造中的设备健康管理提供了新思路和技术支撑。
关键词：智能制造；深度学习；贝叶斯网络；多源异构数据
Abstract
With the advancement of Industry 4.0, intelligent manufacturing has become the core direction for the transformation and upgrading of the manufacturing industry, while predictive maintenance and health management (PHM) have gained significant attention as a key technology to ensure the efficient and stable operation of intelligent production systems. This study aims to construct a PHM system suitable for complex manufacturing environments by integrating advanced data-driven approaches with domain knowledge. Based on multi-source heterogeneous sensor data, a hybrid model combining deep learning and signal processing is proposed, which effectively extracts operational state features of equipment and achieves precise predictions. Meanwhile, Bayesian networks are introduced to optimize uncertainty analysis, thereby enhancing the reliability of prediction results. Experimental results demonstrate that this method exhibits superior performance in various typical industrial scenarios, with a prediction accuracy improvement of over 15% compared to traditional methods, along with strong generalization capabilities. Additionally, a real-time monitoring and decision support module has been developed, providing intelligent recommendations to maintenance personnel and significantly reducing equipment maintenance costs and downtime. The primary innovation of this research lies in the organic combination of deep learning's nonlinear modeling capabilities with the prior advantages of domain knowledge, overcoming the limitations of traditional methods under complex working conditions and offering new insights and technical support for equipment health management in intelligent manufacturing..
Key Words：Intelligent Manufacturing；Deep Learning；Bayesian Network；Multi-Source Heterogeneous Data
目 录
摘 要 I
Abstract II
第1章 绪论 2
1.1 智能制造中设备健康管理的背景与意义 2
1.2 设备故障预测与健康管理的研究现状 2
1.3 本文研究方法与技术路线 3
第2章 设备故障预测的关键技术分析 4
2.1 数据采集与预处理技术 4
2.2 故障特征提取与模式识别 4
2.3 预测模型构建与优化方法 5
第3章 健康管理策略与系统设计 6
3.1 设备健康状态评估体系 6
3.2 基于大数据的健康管理平台架构 6
3.3 实时监控与预警机制设计 7
第4章 智能制造中的应用案例与效果验证 8
4.1 典型智能制造场景分析 8
4.2 故障预测与健康管理的实际应用 8
4.3 系统性能评估与改进方向 9
结 论 9
参考文献 11
致 谢 12
