基于贝叶斯统计的汽车故障诊断模型构建与准确率验证

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中图分类号：U472.9 文献标识码：A 文章编号：1003-8639（2026）02-0159-03

Construction and Accuracy Verification of Automobile Fault Diagnosis Model Based on Bayesian Statistics

Liu Xiaohua (Shangqiu Institute of Technology,Shangqiu 476ooo, China)

【Abstract】The traditional fault diagnosis methods based on experience rules and diagnostic codes have limitations, and the experience of senior maintenance technicians cannot be completelyand accurately formalized,resulting in the diagnosisspeedandacuracybeingafectedbyindividuallevels;formulti-causalandatypical symptom faults,traditional methodsaredificult toefectivelytrace backandperformreasoning.Basedonthis,this paperelaborates onthe importance of constructing anautomobilefault diagnosis model basedon Bayesian statistics and verifying itsaccuracy,analyzes the theoreticalbasisofBaysianstatisticsaditsapplicabilitytofaultdagnosis,details theconstructionprocessoftheBayesian modelfor automobilefaultdiagnosis,and finallconducts modelverificationand performance analysis forreference.

【Key words】Bayesian Statistics;automobile fault diagnosis；mathematical model；accuracy verification

0 引言

汽车产业正处于电动化、智能化、网联化引领的变革阶段，现代汽车搭载大量传感器、执行器及复杂电控单元，系统耦合度高，故障模式呈现多样性、关联性与不确定性。（剩余5258字）