Study on Vehicle Fault Diagnosis Combining Knowledge Graph and XGBoost

胡杰; 陈林; 魏敏; 耿黄政; 张潇; 卿海华; 乔美昀

doi:10.13433/j.cnki.1003-8728.20240027

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Study on Vehicle Fault Diagnosis Combining Knowledge Graph and XGBoost

更新时间：2026-04-23

- Study on Vehicle Fault Diagnosis Combining Knowledge Graph and XGBoost
- Mechanical Science and Technology for Aerospace Engineering Vol. 45, Issue 1, Pages: 163-172(2026)
- 作者机构：
  
  1. 武汉理工大学现代汽车零部件技术湖北省重点实验室机构,武汉,430070
  2. 武汉理工大学现代零部件技术湖北省协同创新中心,武汉,430070
  3. 新能源与智能网联车湖北工程技术研究中心,武汉,430070
  4. 上汽通用五菱汽车股份有限公司,广西,柳州,545007
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20240027
  CLC：
- Published：2026
- 稿件说明：
移动端阅览
胡杰, 陈林, 魏敏, et al. Study on Vehicle Fault Diagnosis Combining Knowledge Graph and XGBoost[J]. Mechanical Science and Technology for Aerospace Engineering, 2026, 45(1): 163-172.
DOI：

胡杰, 陈林, 魏敏, et al. Study on Vehicle Fault Diagnosis Combining Knowledge Graph and XGBoost[J]. Mechanical Science and Technology for Aerospace Engineering, 2026, 45(1): 163-172. DOI： 10.13433/j.cnki.1003-8728.20240027.

摘要

为解决目前车企售后维修存在的过度依赖维修技师经验、维修手册查阅低效和维修历史数据未有效利用等问题，基于某车企闲置的售后维修数据，将知识图谱引入汽车故障领域。鉴于数据中部分字段的文本数据为长文本类型，提出一种基于规则预处理与深度学习模型实体抽取结合的方法，挖掘利用车辆维修历史数据，完成汽车故障知识图谱的构建。为有效利用汽车故障知识图谱协助维修技师进行故障诊断，设计了一种基于知识图谱的车辆故障诊断流程，该流程包含一种融合知识图谱多实体和XGBoost的故障诊断方法。实验对比和实际案例测试分别验证了故障诊断方法的有效性和流程的实际可用性。

Abstract

In order to solve the problems existing in automobile enterprises after-sale maintenance

such as over-reliance on the experience of maintenance technicians

inefficient access to maintenance manuals and ineffective use of maintenance historical data

a knowledge graph in the automobile faults based on idle after-sale maintenance data of an automobile enterprise is introduced. In view of the long text type of some fields in the data

a combination of rule preprocessing and deep learning model entity extraction method is proposed to mine and utilize the vehicle maintenance historical data to complete the construction of vehicle fault knowledge map. In order to effectively utilize the vehicle fault knowledge graph to assist maintenance technicians in fault diagnosis

a vehicle fault diagnosis process based on the knowledge graph is designed

which includes a fault diagnosis method combining the knowledge graph multi-entity and extreme gradient boosting. The effectiveness of the fault diagnosis method and the practical availability of the process are verified by using the experimental comparison and actual case test.

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