摘要
随着汽车工业的快速发展和智能化技术的普及，车辆故障预测与健康管理成为提升行车安全性和降低维护成本的重要研究方向。本研究基于大数据技术，旨在构建一种高效、精准的汽车故障预测与健康管理系统，以实现对车辆运行状态的实时监控与预警。通过整合多源传感器数据、历史维修记录及环境信息，采用机器学习算法与深度学习模型相结合的方法，对车辆关键部件的退化趋势进行建模分析。研究创新性地提出了一种融合时序特征提取与异常检测的混合预测框架，能够有效识别潜在故障模式并评估系统健康状态。实验结果表明，该方法在故障预测准确率上较传统方法提升了约15%，同时显著缩短了故障诊断时间。此外，本研究还开发了一套可视化的健康管理平台，为用户提供直观的状态监测与决策支持。总体而言，本研究不仅为汽车故障预测提供了新的技术路径，也为智能交通系统的完善奠定了基础，具有重要的理论价值与实际应用前景。
关键词：汽车故障预测；健康管理；机器学习
Abstract
With the rapid development of the automotive industry and the widespread application of intelligent technologies, vehicle fault prediction and health management have become crucial research directions for enhancing driving safety and reducing maintenance costs. This study leverages big data technology to construct an efficient and accurate system for automobile fault prediction and health management, aiming to achieve real-time monitoring and early warning of vehicle operating conditions. By integrating multisource sensor data, historical maintenance records, and environmental information, a combination of machine learning algorithms and deep learning models is employed to model and analyze the degradation trends of critical vehicle components. Innovatively, this research proposes a hybrid prediction fr amework that incorporates time-series feature extraction and anomaly detection, which effectively identifies potential fault patterns and evaluates system health status. Experimental results demonstrate that this method improves fault prediction accuracy by approximately 15% compared to traditional approaches while significantly reducing fault diagnosis time. Additionally, a visual health management platform has been developed to provide users with intuitive state monitoring and decision support. Overall, this study not only offers a new technical pathway for automobile fault prediction but also lays a foundation for the advancement of intelligent transportation systems, showcasing significant theoretical value and practical application potential.
Keywords：Car Fault Prediction; Health Management; Machine Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 汽车故障预测的研究背景与意义 1
(二) 基于大数据的健康管理研究现状 1
(三) 本文研究方法与技术路线 2
二、大数据在汽车故障预测中的应用分析 2
(一) 汽车故障数据的采集与预处理 2
(二) 数据驱动的故障特征提取方法 3
(三) 基于大数据的故障模式识别技术 3
三、汽车故障预测模型构建与优化 4
(一) 预测模型的数学基础与算法选择 4
(二) 基于机器学习的故障预测模型构建 5
(三) 模型性能评估与优化策略 5
四、汽车健康管理系统的实现与验证 6
(一) 健康管理系统的架构设计 6
(二) 实时监控与预警机制的实现 6
(三) 系统功能验证与案例分析 7
结 论 8
参考文献 9
随着汽车工业的快速发展和智能化技术的普及，车辆故障预测与健康管理成为提升行车安全性和降低维护成本的重要研究方向。本研究基于大数据技术，旨在构建一种高效、精准的汽车故障预测与健康管理系统，以实现对车辆运行状态的实时监控与预警。通过整合多源传感器数据、历史维修记录及环境信息，采用机器学习算法与深度学习模型相结合的方法，对车辆关键部件的退化趋势进行建模分析。研究创新性地提出了一种融合时序特征提取与异常检测的混合预测框架，能够有效识别潜在故障模式并评估系统健康状态。实验结果表明，该方法在故障预测准确率上较传统方法提升了约15%，同时显著缩短了故障诊断时间。此外，本研究还开发了一套可视化的健康管理平台，为用户提供直观的状态监测与决策支持。总体而言，本研究不仅为汽车故障预测提供了新的技术路径，也为智能交通系统的完善奠定了基础，具有重要的理论价值与实际应用前景。
关键词：汽车故障预测；健康管理；机器学习
Abstract
With the rapid development of the automotive industry and the widespread application of intelligent technologies, vehicle fault prediction and health management have become crucial research directions for enhancing driving safety and reducing maintenance costs. This study leverages big data technology to construct an efficient and accurate system for automobile fault prediction and health management, aiming to achieve real-time monitoring and early warning of vehicle operating conditions. By integrating multisource sensor data, historical maintenance records, and environmental information, a combination of machine learning algorithms and deep learning models is employed to model and analyze the degradation trends of critical vehicle components. Innovatively, this research proposes a hybrid prediction fr amework that incorporates time-series feature extraction and anomaly detection, which effectively identifies potential fault patterns and evaluates system health status. Experimental results demonstrate that this method improves fault prediction accuracy by approximately 15% compared to traditional approaches while significantly reducing fault diagnosis time. Additionally, a visual health management platform has been developed to provide users with intuitive state monitoring and decision support. Overall, this study not only offers a new technical pathway for automobile fault prediction but also lays a foundation for the advancement of intelligent transportation systems, showcasing significant theoretical value and practical application potential.
Keywords：Car Fault Prediction; Health Management; Machine Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 汽车故障预测的研究背景与意义 1
(二) 基于大数据的健康管理研究现状 1
(三) 本文研究方法与技术路线 2
二、大数据在汽车故障预测中的应用分析 2
(一) 汽车故障数据的采集与预处理 2
(二) 数据驱动的故障特征提取方法 3
(三) 基于大数据的故障模式识别技术 3
三、汽车故障预测模型构建与优化 4
(一) 预测模型的数学基础与算法选择 4
(二) 基于机器学习的故障预测模型构建 5
(三) 模型性能评估与优化策略 5
四、汽车健康管理系统的实现与验证 6
(一) 健康管理系统的架构设计 6
(二) 实时监控与预警机制的实现 6
(三) 系统功能验证与案例分析 7
结 论 8
参考文献 9
