机械电子系统中的故障诊断与预测维护策略
摘 要
随着现代工业系统复杂度的不断提升，机械电子系统的可靠性与安全性成为关键问题。为解决传统故障诊断方法难以适应复杂工况、预测精度不足的问题，本文提出了一种基于多源数据融合的故障诊断与预测维护策略。该研究旨在通过整合传感器数据、运行参数及历史维修记录，构建一个全面的故障特征库，并引入深度学习算法进行模式识别与状态评估。具体而言，采用卷积神经网络对振动信号进行特征提取，结合长短期记忆网络实现时序预测，同时引入贝叶斯优化算法优化模型参数。实验结果表明，所提出的诊断模型能够有效识别多种典型故障类型，平均准确率达到93.7%，较传统方法提升12.5%。此外，预测维护模块可提前2 - 4周预警潜在故障，显著降低非计划停机率。
关键词：故障诊断 多源数据融合 深度学习
Abstract
With the continuous improvement of the complexity of modern industrial system, the reliability and safety of mechanical and electronic system have become a key issue. In order to solve the problem that the traditional fault diagnosis methods are difficult to adapt to complex working conditions and the prediction accuracy is insufficient, this paper proposes a fault diagnosis and prediction maintenance strategy based on multi-source data fusion. This study aims to build a comprehensive fault feature library by integrating sensor data, operating parameters and historical maintenance records, and introduce a deep learning algorithm for pattern recognition and status assessment. Specifically, convolutional neural network is used to extract the vibration signal features, long-short-term memory network to realize timing prediction, and Bayesian optimization algorithm is introduced to optimize the model parameters. The experimental results show that the proposed diagnostic model can effectively identify many typical fault types, with an average accuracy of 93.7%, 12.5% higher than the traditional method. In addition, the forecast maintenance module can warn of potential failures 2-4 weeks in advance, significantly reducing the unplanned outage rate.
Keyword:Fault Diagnosis Multi-Source Data Fusion Deep Learning
目 录
1绪论 1
1.1机械电子系统故障诊断的意义 1
1.2国内外研究现状综述 1
1.3本文研究方法与创新点 2
2故障诊断技术基础 2
2.1故障诊断的基本原理 2
2.2常用故障诊断方法 3
2.3数据采集与信号处理 4
3预测维护策略构建 4
3.1预测模型的建立 4
3.2关键性能指标设定 5
3.3维护决策支持系统 6
4实施与案例分析 6
4.1实施方案设计 6
4.2典型案例研究 7
4.3效果评估与优化 8
结论 8
参考文献 10
致谢 11
摘 要
随着现代工业系统复杂度的不断提升，机械电子系统的可靠性与安全性成为关键问题。为解决传统故障诊断方法难以适应复杂工况、预测精度不足的问题，本文提出了一种基于多源数据融合的故障诊断与预测维护策略。该研究旨在通过整合传感器数据、运行参数及历史维修记录，构建一个全面的故障特征库，并引入深度学习算法进行模式识别与状态评估。具体而言，采用卷积神经网络对振动信号进行特征提取，结合长短期记忆网络实现时序预测，同时引入贝叶斯优化算法优化模型参数。实验结果表明，所提出的诊断模型能够有效识别多种典型故障类型，平均准确率达到93.7%，较传统方法提升12.5%。此外，预测维护模块可提前2 - 4周预警潜在故障，显著降低非计划停机率。
关键词：故障诊断 多源数据融合 深度学习
Abstract
With the continuous improvement of the complexity of modern industrial system, the reliability and safety of mechanical and electronic system have become a key issue. In order to solve the problem that the traditional fault diagnosis methods are difficult to adapt to complex working conditions and the prediction accuracy is insufficient, this paper proposes a fault diagnosis and prediction maintenance strategy based on multi-source data fusion. This study aims to build a comprehensive fault feature library by integrating sensor data, operating parameters and historical maintenance records, and introduce a deep learning algorithm for pattern recognition and status assessment. Specifically, convolutional neural network is used to extract the vibration signal features, long-short-term memory network to realize timing prediction, and Bayesian optimization algorithm is introduced to optimize the model parameters. The experimental results show that the proposed diagnostic model can effectively identify many typical fault types, with an average accuracy of 93.7%, 12.5% higher than the traditional method. In addition, the forecast maintenance module can warn of potential failures 2-4 weeks in advance, significantly reducing the unplanned outage rate.
Keyword:Fault Diagnosis Multi-Source Data Fusion Deep Learning
目 录
1绪论 1
1.1机械电子系统故障诊断的意义 1
1.2国内外研究现状综述 1
1.3本文研究方法与创新点 2
2故障诊断技术基础 2
2.1故障诊断的基本原理 2
2.2常用故障诊断方法 3
2.3数据采集与信号处理 4
3预测维护策略构建 4
3.1预测模型的建立 4
3.2关键性能指标设定 5
3.3维护决策支持系统 6
4实施与案例分析 6
4.1实施方案设计 6
4.2典型案例研究 7
4.3效果评估与优化 8
结论 8
参考文献 10
致谢 11
