摘 要
液压系统作为现代工业中的核心动力传输技术，广泛应用于工程机械、航空航天和制造业等领域，其运行可靠性直接影响设备性能与安全性。然而，由于工作环境复杂及零部件老化等因素，液压系统故障频发，成为制约设备高效运行的关键问题。为此，本文以提升液压系统故障诊断效率和维修策略优化为目标，综合运用数据驱动方法与机理分析手段，提出了一种基于多源信号融合的故障诊断框架，并结合预测性维护理念设计了针对性的维修方案。研究中采用传感器采集压力、流量和温度等动态参数，通过特征提取与模式识别算法实现对典型故障类型的精准分类，同时引入深度学习模型提高诊断精度。实验结果表明，该方法能够有效识别液压泵磨损、阀芯卡滞和管路泄漏等多种故障类型，诊断准确率较传统方法提升约15%。此外，所提出的维修策略通过量化故障发展趋势，为制定合理的维护计划提供了科学依据，显著降低了非计划停机时间与维修成本。本研究的创新点在于将多源信息融合与智能算法相结合，突破了单一信号诊断局限性，为液压系统的智能化运维奠定了理论基础，具有重要的工程应用价值。关键词：液压系统故障诊断；多源信号融合；深度学习；预测性维护；维修策略优化
Abstract
Hydraulic systems, as a core power transmission technology in modern industry, are widely applied in engineering machinery, aerospace, and manufacturing sectors, where their operational reliability directly affects equipment performance and safety. However, due to complex working environments and component aging, hydraulic system failures occur frequently, becoming a critical issue that constrains the efficient operation of equipment. To address this, this study aims to enhance the efficiency of fault diagnosis and optimize maintenance strategies for hydraulic systems by integrating data-driven approaches with mechanism analysis. A fault diagnosis fr amework based on multi-source signal fusion is proposed, combined with predictive maintenance concepts to design targeted repair schemes. Dynamic parameters such as pressure, flow rate, and temperature are collected using sensors, and feature extraction and pattern recognition algorithms are employed to achieve precise classification of typical fault types. Additionally, deep learning models are introduced to improve diagnostic accuracy. Experimental results demonstrate that this method can effectively identify various fault types, including pump wear, valve sticking, and pipeline leakage, with a diagnostic accuracy approximately 15% higher than traditional methods. Furthermore, the proposed maintenance strategy, by quantifying fault development trends, provides a scientific basis for formulating reasonable maintenance plans, significantly reducing unplanned downtime and maintenance costs. The innovation of this research lies in the integration of multi-source information fusion with intelligent algorithms, overcoming the limitations of single-signal diagnosis and laying a theoretical foundation for the intelligent operation and maintenance of hydraulic systems, thus possessing significant engineering application value..
Key Words：Hydraulic System Fault Diagnosis；Multi-Source Signal Fusion；Deep Learning；Predictive Maintenance；Maintenance Strategy Optimization
目 录
摘 要 I
Abstract II
第1章 绪论 2
1.1 液压系统故障分析的研究背景与意义 2
1.2 国内外液压系统故障研究现状综述 2
1.3 本文研究方法与技术路线设计 3
第2章 液压系统故障类型与成因分析 4
2.1 常见液压系统故障类型分类 4
2.2 液压系统故障的主要成因探讨 4
2.3 故障类型与成因的关联性研究 5
第3章 液压系统故障诊断技术与方法 6
3.1 基于传感器的故障数据采集技术 6
3.2 数据驱动的故障诊断模型构建 6
3.3 先进诊断技术在液压系统的应用分析 7
第4章 液压系统维修策略与优化方案 9
4.1 预防性维修策略的设计与实施 9
4.2 基于故障数据分析的维修决策优化 9
4.3 维修策略对系统性能的影响评估 10
结 论 10
参考文献 12
致 谢 13
