摘 要
随着现代交通系统的快速发展，车辆故障诊断与健康管理成为提升运输效率和安全性的重要手段。本研究基于大数据技术，提出了一种集成化的车辆故障诊断与健康管理系统，旨在通过实时数据采集、分析与预测，实现对车辆运行状态的全面监控与精准评估。研究采用多源传感器数据融合方法，结合机器学习算法与深度神经网络模型，构建了高效的故障特征提取与分类框架，并引入健康指数评估体系以量化车辆性能退化程度。实验结果表明，该系统能够准确识别多种典型故障模式，其诊断精度达到95%以上，同时具备较强的泛化能力和实时性优势。此外，系统还支持长期趋势预测，为预防性维护提供了科学依据。本研究的主要创新点在于将大数据分析与传统诊断技术深度融合，突破了单一数据源局限性，显著提升了诊断的可靠性和智能化水平。研究成果不仅为车辆运维管理提供了新思路，也为复杂装备的状态监测与健康管理领域拓展了应用前景。
关键词：车辆故障诊断；健康管理系统；大数据技术；机器学习；健康指数评估
Abstract
With the rapid development of modern transportation systems, vehicle fault diagnosis and health management have become critical approaches to enhancing transportation efficiency and safety. This study proposes an integrated vehicle fault diagnosis and health management system based on big data technology, aiming to achieve comprehensive monitoring and precise evaluation of vehicle operating conditions through real-time data acquisition, analysis, and prediction. A multi-source sensor data fusion method is employed in conjunction with machine learning algorithms and deep neural network models to construct an efficient fault feature extraction and classification fr amework. Additionally, a health index assessment system is introduced to quantify the degree of vehicle performance degradation. Experimental results demonstrate that the system can accurately identify various typical fault patterns, achieving a diagnostic accuracy of over 95%, while also exhibiting strong generalization capabilities and real-time advantages. Furthermore, the system supports long-term trend forecasting, providing a scientific basis for preventive maintenance. The primary innovation of this research lies in the deep integration of big data analytics with traditional diagnostic techniques, overcoming the limitations of single data sources and significantly improving the reliability and intelligence level of diagnostics. The research findings not only offer new insights into vehicle operation and maintenance management but also broaden the application prospects in the field of condition monitoring and health management for complex equipment.
Keywords: Vehicle Fault Diagnosis；Health Management System；Big Data Technology；Machine Learning；Health Index Evaluation
目 录
摘 要 I
Abstract II
一、绪论 1
（一）《基于大数据的车辆故障诊断与健康管理系统》的研究背景 1
（二）《基于大数据的车辆故障诊断与健康管理系统》的研究意义 1
（三）国内外研究现状分析 1
（四）本文研究方法与技术路线 2
二、大数据在车辆故障诊断中的应用 2
（一）车辆故障数据采集与预处理 2
（二）数据驱动的故障模式识别方法 2
（三）基于机器学习的故障预测模型构建 3
（四）实验验证与结果分析 4
三、健康管理系统的架构设计与实现 4
（一）系统功能需求分析 4
（二）数据存储与管理方案设计 5
（三）健康评估算法的设计与优化 5
（四）系统性能测试与评价 6
四、实际案例分析与系统优化 6
（一）典型应用场景分析 6
（二）系统运行效果评估 6
（三）用户反馈与改进建议 7
（四）面向未来的优化方向 7
结 论 8
致 谢 9
参考文献 10
