Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6): 190-195.doi: 10.11896/jsjkx.200700204

• Artificial Intelligence • Previous Articles     Next Articles

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

LEI Jian-mei1,2, ZENG Ling-qiu1,3, MU Jie2, CHEN Li-dong1, WANG Cong1, CHAI Yong2   

  1. 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).

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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

CLC Number: 

  • TP206+.3
[1]LEI J,YAN H,CHEN R,et al.Automotive mmWave Radar EMC Test Developments and Challenges[C]//Proceedings of 2019 13th European Conference on Antennas and Propagation(EuCAP).IEEE/IET Electronic Library(IEL),2019:1-6.
[2]ZHENG J Q.EMC Risk Assessment in Product Design[J].Electrical & Energy Management Technology,2017(14):35-37,73.
[3]LIANG X L,CHEN T,YANG X D.Application of Fuzzy Fault Tree Analysis in the Fault Diagnosis for Automobile Engine System[C]//2017 9th International Conference on Modelling,Identification and Control(ICMIC).IEEE,2017:711-716.
[4]LI G,LIU P,LI Y,et al.Techniques of Electromagnetic Compatibility Model SynthesisBased on On-Site Measurement Data [J].Journal of Microwaves,2013,96(9):2251-2260.
[5]HE J,YANG S,GAN C.Unsupervised Fault Diagnosis of aGear Transmission Chain Using a Deep Belief Network [J].Sensors,2017,17(7):1564.
[6]SILVA M,SANTOS A,FIGUEIREDO E.Damage Detection for Structural Health Monitoring of Bridges as a Knowledge Disco-very in Databases Process [M]//Data Mining in Structural Dynamic Analysis:A Signal Processing Perspective.Singapore:Springer Singapore.2019:1-24.
[7]YIN Z,HOU J.Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes [J].Neurocomputing,2016,174(JAN.22PT.B):643-650.
[8]GUO C Y,OUYANG G Y,LI Y F,Fault diagnosis of circuit in small sample based on artificial immune system [J].Journal of Electronic Measurement and Instrument,2010,24(5):425-429.
[9]ZHAO L H,ZHOU L H,DAI Y P,et al.The application and development of artificial immune system in rotating machinery fault diagnosis [J].Modern Manufacturing Engineering,2015(7):143-149.
[10] MAGAGULA X G,HAMAM Y,JORDAAN J A,et al.A fault classification and localization method in a power distribution network[C]//2017 IEEE AFRICON.IEEE,2017:1337-1343.
[11]GALAL M A,HUSSEIN W M,KAWY E E D A,et al.Satellite battery fault detection using Naive Bayesian classifier[C]//2019 IEEE Aerospace Conference.IEEE,2019:1-11.
[12] SOWAH R A,DZABENG N A,OFOLI A R,et al.Design of Power Distribution Network Fault Data Collector for Fault Detection,Location and Classification using Machine Learning[C]//2018 IEEE 7th International Conference on Adaptive Science & Technology(ICAST).IEEE,2018:1-8.
[13]CAO H L,XUE C,XUE P.Application of SVM Based on Multiple Kernels Learning in Engine Fault Diagnosis [J].Machinery Design & Manufacture,2019,339(5):8-11,16.
[14]WANG B J,ZHANG X L,FU Y A X,et al.Optimization of support vector machine and its application in intelligent fault diagnosis.[J].Journal of Vibration,Measurement & Diagnosis,2017,37(3):547-552.
[15]HAN F,TIAN Y,ZOU Q,et al.Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System [J].ENERGIES,2020,13(10):2531.
[16]REBALA G,RAVI A,CHURIWALA S.Principal Component Analysis[M]//An Introduction to Machine Learning.Cham,Springer International Publishing.2019:141-52.
[17]BELL J.Machine learning:hands-on for developers and technical professionals [M].John Wiley & Sons,2015.
[18]VAPNIK V,VAPNIK V,VAPNIK V N.Statistical LearningTheory [J].Annals of the Institute of Statistical Mathematics,2003,55(2):371-389.
[19]LI R,SU H,WANG Z,et al.Medium-and long-term load forecasting based on heuristic least square support vector machine [J].Power System Technology,2011,35(11):195-199.
[20]DA L.An Intrusion Detection System Based on Naive BayesClassifier [J].Cyberspace Security,2017(8):32-34.
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