基于SVM算法的汽车空调设备制冷剂泄漏故障预测方法  

Prediction Method for Refrigerant Leakage Faults in Automotive Air Conditioning Equipment Based on SVM Algorithm

作　　者：杨超[1] Yang Chao(Baise Vocational College,Guangxi Baise 533000)

机构地区：[1]百色职业学院,广西百色533000

出　　处：《内燃机与配件》2024年第20期60-62,共3页Internal Combustion Engine & Parts

基　　金：2021年度广西高校中青年教师科研基础能力提升项目“基于模糊识别的制冷系统故障诊断研究”(2021KY1477)。

摘　　要：信息技术的发展为人们的日常生产、生活提供了极为便利的条件,也为各行各业的技术体系升级和转型奠定了良好基础,本文则是以提升汽车空调设备制冷剂泄漏检测质量和时效性为目的,围绕着SVM算法进行分析,明确了目前数据挖掘在制冷剂泄漏故障诊断领域的研究现状,综合SVM算法的具体原理和应用细节,从数据预处理、故障预测模型设计以及预测实验的角度进行分析。结果表明,SVM算法对于汽车空调设备制冷剂泄漏故障预测有着较强的时效性,故障检测的时间更短,预测效果更强,有助于提升空调设备系统的运维安全性和有效性。The development of information technology has provided extremely convenient conditions for people's daily production and life,and has also laid a good foundation for the upgrading and transformation of technical systems in various industries.This article aims to improve the quality and timeliness of refrigerant leakage detection in automotive air conditioning equipment,analyze the SVM algorithm,clarify the current research status of data mining in the field of refrigerant leakage fault diagnosis,and comprehensively analyze the specific principles and application details of SVM algorithm from the perspectives of data preprocessing,fault prediction model design,and prediction experiments.The results show that SVM algorithm has strong timeliness for predicting refrigerant leakage faults in automotive air conditioning equipment,with shorter fault detection time and stronger prediction effect,which helps to improve the operational safety and effectiveness of air conditioning equipment systems.

关 键 词：SVM算法 汽车空调设备 制冷剂 线路故障 预测 

分 类 号：U472[机械工程—车辆工程]