摘 要
伺服控制系统作为现代工业自动化的核心组成部分，其性能优劣直接影响生产效率与产品质量。随着工业系统复杂性的提升，故障诊断与可靠性研究成为保障伺服控制系统稳定运行的关键领域。本研究以提高伺服控制系统的故障诊断精度和可靠性为目标，深入分析了系统中常见故障类型及其传播机理，并提出了一种基于多源信息融合的智能故障诊断方法。该方法结合数据驱动与模型驱动的优势，通过引入深度学习算法对系统运行状态进行实时监测与评估，同时利用贝叶斯网络实现不确定性推理，从而显著提升了故障识别的准确性和时效性。此外，本研究还构建了一套完整的可靠性评估体系，综合考虑了系统硬件老化、环境干扰及操作误差等因素，为优化系统设计提供了理论依据。实验结果表明，所提出的故障诊断方法在多种工况下均表现出优异的性能，且可靠性评估模型能够有效预测系统寿命并指导维护决策。
关键词：伺服控制系统 故障诊断 多源信息融合
Abstract
As the core component of modern industrial automation, the performance of servo control system directly affects the production efficiency and product quality. With the increase of industrial system complexity, the fault diagnosis and reliability research become the key field to guarantee the stable operation of servo control system. To improve the accuracy and reliability of the servo control system, analyze common fault types and their propagation mechanism, and proposed an intelligent fault diagnosis method based on multi-source information fusion. By combining the advantages of data driven and model driven, this method introduces deep learning algorithm to monitor and evaluate the running state of the system in real time, and uses Bayesian network to realize uncertainty inference, thus significantly improving the accuracy and timeliness of fault identification. In addition, this study also establishes a complete reliability evaluation system, which comprehensively considers the system hardware aging, environmental interference and operation error, and provides a theoretical basis for optimizing the system design. The experimental results show that the proposed fault diagnosis method shows excellent performance under various working conditions, and the reliability evaluation model can effectively predict the system life and guide the maintenance decision.
Keyword:Servo Control System Fault Diagnosis Multi-Source Information Fusion
目 录
1绪论 1
1.1伺服控制系统的研究背景与意义 1
1.2故障诊断与可靠性研究的现状分析 1
1.3本文研究方法与技术路线 2
2伺服控制系统的故障特征分析 2
2.1故障类型及其分类方法 2
2.2故障特征提取的关键技术 3
2.3数据驱动的故障特征建模 3
2.4实验验证与结果分析 4
3伺服控制系统的故障诊断方法研究 4
3.1基于信号处理的故障诊断算法 4
3.2智能算法在故障诊断中的应用 5
3.3多传感器数据融合的诊断策略 5
3.4实际案例分析与效果评估 6
4伺服控制系统的可靠性评估与优化 6
4.1可靠性评估的基本理论与方法 6
4.2影响系统可靠性的关键因素分析 7
4.3基于预测模型的可靠性优化策略 7
4.4系统寿命预测与健康管理技术 8
结论 8
参考文献 10
致谢 11
