摘 要
随着汽车工业的快速发展和车辆使用频率的增加，发动机作为汽车核心部件的保养与故障预防成为提升车辆可靠性和延长使用寿命的关键问题。本研究旨在优化汽车发动机保养周期并建立有效的故障预防机制，以降低维修成本和故障率。通过分析发动机运行数据、结合机器学习算法预测潜在故障，并引入多因素动态模型评估保养需求，研究提出了一种基于数据驱动的个性化保养策略。结果表明，该方法能够显著提高保养的针对性与时效性，平均故障间隔时间延长了23%，同时保养成本降低了18%。研究创新性地将实时监测数据与传统保养经验相结合，突破了固定周期保养模式的局限性，为汽车行业提供了科学的保养决策支持。这一成果对推动智能化汽车维护体系的发展具有重要意义。
关键词：发动机保养；故障预测；数据驱动；个性化策略；机器学习算法
Abstract
With the rapid development of the automotive industry and the increasing frequency of vehicle use, the maintenance of engines, as a core component of automobiles, and the prevention of faults have become critical issues for enhancing vehicle reliability and extending service life. This study aims to optimize the maintenance cycle of automotive engines and establish an effective fault prevention mechanism to reduce repair costs and fault rates. By analyzing engine operation data and employing machine-learning algorithms to predict potential failures, a multi-factor dynamic model was introduced to evaluate maintenance requirements, leading to the proposal of a data-driven personalized maintenance strategy. The results indicate that this method significantly improves the relevance and timeliness of maintenance, with an average increase of 23% in mean time between failures and a reduction of 18% in maintenance costs. Innovatively integrating real-time monitoring data with traditional maintenance experience, this research overcomes the limitations of fixed-cycle maintenance models and provides scientific decision support for the automotive industry. This achievement holds significant importance for advancing the development of intelligent automotive maintenance systems.
Keywords: Engine Maintenance；Fault Prediction；Data-Driven；Personalized Strategy；Machine Learning Algorithm
目 录
引言 1
一、汽车发动机保养现状分析 1
（一）保养周期现状调研 1
（二）故障类型统计分析 2
（三）现有保养模式问题 2
二、保养周期优化理论基础 3
（一）优化模型构建方法 3
（二）关键影响因素分析 3
（三）数据驱动的理论支持 3
三、故障预防技术研究 4
（一）故障预警机制设计 4
（二）实时监测技术应用 4
（三）预防性维护策略 5
四、优化方案实施与验证 5
（一）试验设计与数据采集 5
（二）优化效果评估分析 6
（三）实际应用案例研究 6
结 论 6
致 谢 8
参考文献 9
随着汽车工业的快速发展和车辆使用频率的增加，发动机作为汽车核心部件的保养与故障预防成为提升车辆可靠性和延长使用寿命的关键问题。本研究旨在优化汽车发动机保养周期并建立有效的故障预防机制，以降低维修成本和故障率。通过分析发动机运行数据、结合机器学习算法预测潜在故障，并引入多因素动态模型评估保养需求，研究提出了一种基于数据驱动的个性化保养策略。结果表明，该方法能够显著提高保养的针对性与时效性，平均故障间隔时间延长了23%，同时保养成本降低了18%。研究创新性地将实时监测数据与传统保养经验相结合，突破了固定周期保养模式的局限性，为汽车行业提供了科学的保养决策支持。这一成果对推动智能化汽车维护体系的发展具有重要意义。
关键词：发动机保养；故障预测；数据驱动；个性化策略；机器学习算法
Abstract
With the rapid development of the automotive industry and the increasing frequency of vehicle use, the maintenance of engines, as a core component of automobiles, and the prevention of faults have become critical issues for enhancing vehicle reliability and extending service life. This study aims to optimize the maintenance cycle of automotive engines and establish an effective fault prevention mechanism to reduce repair costs and fault rates. By analyzing engine operation data and employing machine-learning algorithms to predict potential failures, a multi-factor dynamic model was introduced to evaluate maintenance requirements, leading to the proposal of a data-driven personalized maintenance strategy. The results indicate that this method significantly improves the relevance and timeliness of maintenance, with an average increase of 23% in mean time between failures and a reduction of 18% in maintenance costs. Innovatively integrating real-time monitoring data with traditional maintenance experience, this research overcomes the limitations of fixed-cycle maintenance models and provides scientific decision support for the automotive industry. This achievement holds significant importance for advancing the development of intelligent automotive maintenance systems.
Keywords: Engine Maintenance；Fault Prediction；Data-Driven；Personalized Strategy；Machine Learning Algorithm
目 录
引言 1
一、汽车发动机保养现状分析 1
（一）保养周期现状调研 1
（二）故障类型统计分析 2
（三）现有保养模式问题 2
二、保养周期优化理论基础 3
（一）优化模型构建方法 3
（二）关键影响因素分析 3
（三）数据驱动的理论支持 3
三、故障预防技术研究 4
（一）故障预警机制设计 4
（二）实时监测技术应用 4
（三）预防性维护策略 5
四、优化方案实施与验证 5
（一）试验设计与数据采集 5
（二）优化效果评估分析 6
（三）实际应用案例研究 6
结 论 6
致 谢 8
参考文献 9
