基于整车EMC标准测试和机器学习的反向诊断方法

doi:10.11896/jsjkx.200700204

计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 190-195.doi: 10.11896/jsjkx.200700204

基于整车EMC标准测试和机器学习的反向诊断方法

雷剑梅^1,2, 曾令秋^1,3, 牟洁², 陈立东¹, 王淙¹, 柴勇²

1 汽车噪声振动和安全技术国家重点实验室重庆400044
2 重庆大学汽车工程学院重庆400044
3 重庆大学计算机学院重庆 400044

收稿日期:2020-07-31 修回日期:2020-09-17 出版日期:2021-06-15 发布日期:2021-06-03
通讯作者: 曾令秋(zenglq@cqu.edu.cn)
基金资助:
汽车噪声振动和安全技术国家重点实验室2019年度开放基金(NVHSKL-201913);国家自然科学基金(61601066)

Reverse Diagnostic Method Based on Vehicle EMC Standard Test and Machine Learning

LEI Jian-mei^1,2, ZENG Ling-qiu^1,3, MU Jie², CHEN Li-dong¹, WANG Cong¹, CHAI Yong²

1 State Key Laboratory of Vehicle NVH and Safety Technology,Chongqing 400044,China
2 School of Automotive Engineering,Chongqing University,Chongqing 400044,China
3 College of Computer Science,Chongqing University,Chongqing 400044,China

Received:2020-07-31 Revised:2020-09-17 Online:2021-06-15 Published:2021-06-03
About author:LEI Jian-mei,born in 1978,Ph.D,professor-level senior engineer.Her main research interests include vehicle-level EMC perfor-mance analysis and prediction,and automotive RF electronics and test technologies in intelligent connected vehicles and prediction,and automotive RF electronics and test technologies in intelligent connected vehicles.(leijianmei@caeri.com.cn)
ZENG Ling-qiu,born in 1975,Ph.D,professor.His main research interests include VANET,intelligent transportation,and data processing.
Supported by:
Open Research Fund of State Key Laboratory of Vehicle NVH and Safety Technology(NVHSKL-201913) and National Natural Science Foundation of China(61601066).

摘要/Abstract

摘要： 智能汽车的快速发展促使电磁兼容(Electromagnetic Compatibility,EMC)测试技术得以完善,同时也给车辆EMC设计带来了新的挑战,而面向测试数据的故障排查有利于车辆EMC的设计。随着电子系统复杂性的提升,车载系统设计人员面临着越来越多的电磁兼容故障可能性,因此需要更为有效的EMC故障诊断方法。然而,由于EMC测试数据集具有样本小、非线性、高维等特点,致使EMC故障诊断难度较大。鉴于此,结合EMC测试工程师多年的整改经验,文中提出了一种关于电磁兼容测试数据的特征提取算法,并利用从测试数据中提取出的有价值的特征数据,搭建了支持向量机二分类模型,实现了EMC故障分类,并展示了相应的应用效果。为了验证所提方法的有效性,采用朴素贝叶斯分类模型进行对比,实验结果表明,所提方法能够满足智能汽车EMC故障诊断的需求。

关键词: EMC, 测试数据, 故障诊断, 朴素贝叶斯, 特征提取, 支持向量机

Abstract: The rapid development of intelligent vehicles not only improves electromagnetic compatibility(EMC) testing technology,but also brings new challenges to vehicle EMC design,which is benefited from test data-oriented troubleshooting.With the increase in electronic complexity,vehicle on-board system designers should confront with more and more EMC failure possibilities and they are in need of effective EMC failure diagnosis approach.However,EMC fault diagnosis is difficult due to the distinguishing features of EMC test dataset,such as small sample,nonlinear,high dimensions,etc.In view of this situation,this paper puts forward a feature extraction algorithm for electromagnetic compatibility test data based on years of rectification experience of EMC test engineers,and uses the valuable feature data extracted from the test data to set up a support vector machine(SVM) two classification model.Corresponding application effect is displayed.In order to verify the effectiveness of the proposed method,this paper adopts the naive Bayesian classification model for comparison.The experimental results show that the proposed method can match the demand of EMC fault diagnosis for intelligent vehicles.

Key words: Electromagnetic compatibility, Fault diagnosis, Feature extraction, Naive Bayes, Support vector machine, Test data

中图分类号:

TP206+.3

雷剑梅, 曾令秋, 牟洁, 陈立东, 王淙, 柴勇. 基于整车EMC标准测试和机器学习的反向诊断方法[J]. 计算机科学, 2021, 48(6): 190-195. https://doi.org/10.11896/jsjkx.200700204

LEI Jian-mei, ZENG Ling-qiu, MU Jie, CHEN Li-dong, WANG Cong, CHAI Yong. Reverse Diagnostic Method Based on Vehicle EMC Standard Test and Machine Learning[J]. Computer Science, 2021, 48(6): 190-195. https://doi.org/10.11896/jsjkx.200700204

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基于整车EMC标准测试和机器学习的反向诊断方法

Reverse Diagnostic Method Based on Vehicle EMC Standard Test and Machine Learning

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