摘 要
随着制造业向智能化转型，自动化装配系统在提高生产效率和产品质量方面发挥着重要作用，但其故障问题也日益凸显。为此，本文聚焦于自动化装配中的故障预测与健康管理技术研究，旨在通过构建有效的故障预测模型及健康管理系统，实现对设备状态的实时监测、故障早期预警以及剩余寿命预测，从而降低非计划停机时间并优化维护策略。基于此目的，采用数据驱动与机理分析相结合的方法，利用传感器采集的数据，融合信号处理、特征提取、机器学习算法等多学科知识，建立适用于复杂工况下自动化装配系统的故障预测模型，并设计了包含状态评估、故障诊断、趋势预测等功能模块的健康管理系统。实验结果表明，所提出的故障预测模型能够准确识别潜在故障模式，提前发现异常征兆，平均预测精度达到90%以上；健康管理系统可有效跟踪设备退化过程，为制定合理的维护计划提供依据。
关键词：故障预测 健康管理 自动化装配系统
Abstract
As manufacturing transitions towards smart transformation, automated assembly systems play a crucial role in enhancing production efficiency and product quality; however, fault issues have become increasingly prominent. This paper focuses on the research of fault prediction and health management technology in automated assembly, aiming to establish effective fault prediction models and health management systems to achieve real-time monitoring of equipment status, early warning of faults, and prediction of remaining useful life, thereby reducing unplanned downtime and optimizing maintenance strategies. To achieve this ob jective, a method combining data-driven approaches with mechanistic analysis is adopted, utilizing data collected from sensors and integrating multidisciplinary knowledge such as signal processing, feature extraction, and machine learning algorithms to develop fault prediction models suitable for complex operating conditions in automated assembly systems. Additionally, a health management system incorporating functional modules for condition assessment, fault diagnosis, and trend prediction has been designed. Experimental results indicate that the proposed fault prediction model can accurately identify potential fault patterns and detect early signs of anomalies, achieving an average prediction accuracy of over 90%; the health management system effectively tracks the degradation process of equipment, providing a basis for formulating reasonable maintenance plans.
Keyword:Fault Prediction Health Management Automation Assembly System
目 录
1绪论 1
1.1自动化装配故障预测的意义 1
1.2国内外研究现状综述 1
1.3本文研究方法与思路 1
2故障预测技术基础 2
2.1故障预测理论框架 2
2.2关键技术原理分析 3
2.3数据采集与处理方法 3
3健康管理技术体系 4
3.1健康状态评估模型 4
3.2预测性维护策略 4
3.3系统可靠性优化 5
4应用案例与效果分析 6
4.1实际应用案例介绍 6
4.2技术实施过程解析 6
4.3应用效果评估分析 7
结论 7
参考文献 9
致谢 10
