摘 要
随着工业4.0的推进和智能制造技术的发展，智能机械装备的高效运行与可靠性保障成为关键课题，而故障诊断作为提升设备性能和降低维护成本的核心手段备受关注本研究旨在开发一种基于多源数据融合与深度学习的智能机械装备故障诊断系统，以实现对复杂工况下设备故障的精准识别与预测研究中采用传感器采集振动、温度及压力等多维度运行数据，并通过信号预处理提取特征信息结合卷积神经网络与长短时记忆网络构建混合模型，有效捕捉数据的空间与时间特性同时引入迁移学习方法，解决实际应用场景中数据样本不足的问题实验结果表明，该系统在多种典型故障模式下的诊断准确率超过95%，显著优于传统方法此外，系统具备良好的泛化能力，可适应不同类型的机械设备这一研究成果不仅为智能机械装备的故障诊断提供了新思路。
关键词：智能机械装备 故障诊断 多源数据融合
Abstract
With the advancement of Industry 4.0 and the development of intelligent manufacturing technology, The efficient operation and reliability guarantee of intelligent machinery and equipment have become a key topic, The purpose of this research is to develop an intelligent mechanical equipment fault diagnosis system based on multi-source data fusion and deep learning, In order to realize the accurate identification and prediction of equipment faults under complex working conditions, sensors are used to collect multi-dimensional operation data such as vibration, temperature and pressure, Through signal preprocessing to extract feature information combined with convolutional neural network and long and short memory network, The spatial and temporal characteristics of effectively captured data simultaneously introduce transfer learning methods, Experimental results on addressing the problem of insufficient data samples in practical application scenarios show that, The system has diagnostic accuracy over 95% in multiple typical failure modes, Significantly better than the conventional methods in addition, The system has a good generalization ability, The research results that can adapt to different types of mechanical equipment not only provide a new idea for the fault diagnosis of intelligent mechanical equipment.
Keyword:Intelligent Mechanical Equipment Fault Diagnosis Multi-Source Data Fusion
目 录
1绪论 1
1.1智能机械装备故障诊断的研究背景 1
1.2故障诊断系统开发的意义与价值 1
1.3国内外研究现状分析 1
1.4本文研究方法与技术路线 2
2故障诊断系统需求分析 2
2.1智能机械装备的运行特性分析 2
2.2故障诊断系统的功能需求 2
2.3数据采集与处理的技术要求 3
2.4系统性能指标与约束条件 3
3故障诊断系统设计与实现 4
3.1系统架构设计原则与方法 4
3.2核心算法的选择与优化 4
3.3软硬件协同设计策略 5
3.4系统集成与测试方案 5
4故障诊断系统验证与应用 6
4.1实验平台搭建与数据获取 6
4.2系统性能评估与结果分析 6
4.3典型故障案例诊断分析 7
4.4系统改进方向与优化建议 7
结论 7
参考文献 9
致谢 10
随着工业4.0的推进和智能制造技术的发展，智能机械装备的高效运行与可靠性保障成为关键课题，而故障诊断作为提升设备性能和降低维护成本的核心手段备受关注本研究旨在开发一种基于多源数据融合与深度学习的智能机械装备故障诊断系统，以实现对复杂工况下设备故障的精准识别与预测研究中采用传感器采集振动、温度及压力等多维度运行数据，并通过信号预处理提取特征信息结合卷积神经网络与长短时记忆网络构建混合模型，有效捕捉数据的空间与时间特性同时引入迁移学习方法，解决实际应用场景中数据样本不足的问题实验结果表明，该系统在多种典型故障模式下的诊断准确率超过95%，显著优于传统方法此外，系统具备良好的泛化能力，可适应不同类型的机械设备这一研究成果不仅为智能机械装备的故障诊断提供了新思路。
关键词：智能机械装备 故障诊断 多源数据融合
Abstract
With the advancement of Industry 4.0 and the development of intelligent manufacturing technology, The efficient operation and reliability guarantee of intelligent machinery and equipment have become a key topic, The purpose of this research is to develop an intelligent mechanical equipment fault diagnosis system based on multi-source data fusion and deep learning, In order to realize the accurate identification and prediction of equipment faults under complex working conditions, sensors are used to collect multi-dimensional operation data such as vibration, temperature and pressure, Through signal preprocessing to extract feature information combined with convolutional neural network and long and short memory network, The spatial and temporal characteristics of effectively captured data simultaneously introduce transfer learning methods, Experimental results on addressing the problem of insufficient data samples in practical application scenarios show that, The system has diagnostic accuracy over 95% in multiple typical failure modes, Significantly better than the conventional methods in addition, The system has a good generalization ability, The research results that can adapt to different types of mechanical equipment not only provide a new idea for the fault diagnosis of intelligent mechanical equipment.
Keyword:Intelligent Mechanical Equipment Fault Diagnosis Multi-Source Data Fusion
目 录
1绪论 1
1.1智能机械装备故障诊断的研究背景 1
1.2故障诊断系统开发的意义与价值 1
1.3国内外研究现状分析 1
1.4本文研究方法与技术路线 2
2故障诊断系统需求分析 2
2.1智能机械装备的运行特性分析 2
2.2故障诊断系统的功能需求 2
2.3数据采集与处理的技术要求 3
2.4系统性能指标与约束条件 3
3故障诊断系统设计与实现 4
3.1系统架构设计原则与方法 4
3.2核心算法的选择与优化 4
3.3软硬件协同设计策略 5
3.4系统集成与测试方案 5
4故障诊断系统验证与应用 6
4.1实验平台搭建与数据获取 6
4.2系统性能评估与结果分析 6
4.3典型故障案例诊断分析 7
4.4系统改进方向与优化建议 7
结论 7
参考文献 9
致谢 10
