Prediction of Automobile Wiper Motor Noise Based on Support Vector Machine with Vibration Sensors.


Journal

Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357

Informations de publication

Date de publication:
2022
Historique:
received: 16 12 2021
accepted: 07 03 2022
entrez: 11 4 2022
pubmed: 12 4 2022
medline: 13 4 2022
Statut: epublish

Résumé

Wiper motor noise has an important impact on vehicle comfort. Accurate prediction of wiper motor noise can obtain motor NVH performance in motor manufacturing or earlier stage and provide necessary support for NVH performance design of parts and vehicles. However, the prediction accuracy of wiper motor noise by the traditional CAE or testing method is low. Data-driven technology provides a new idea for wiper motor noise prediction with its advantages of high efficiency and high precision. This paper studies the wiper motor noise prediction algorithm based on the motor vibration signal, respectively, using the transmission path analysis theory and the support vector machine theory, and carries on the test verification and comparative analysis of the effect. The results show that the method based on support vector machine is more accurate in the prediction of wiper motor noise and has higher practical engineering value.

Identifiants

pubmed: 35401718
doi: 10.1155/2022/3873651
pmc: PMC8989595
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3873651

Informations de copyright

Copyright © 2022 Haiqing Li et al.

Déclaration de conflit d'intérêts

The authors declare that they have no conflicts of interest.

Références

Comput Intell Neurosci. 2020 Aug 1;2020:8860841
pubmed: 32802030
IEEE Trans Neural Netw Learn Syst. 2021 Aug;32(8):3566-3577
pubmed: 32822307

Auteurs

Haiqing Li (H)

Liuzhou Vocational and Technical College, Liuzhou 545006, China.
National Laboratory for Rail Transportation, Southwest Jiaotong University, Chengdu 610031, China.

Zhanhao Cui (Z)

National Laboratory for Rail Transportation, Southwest Jiaotong University, Chengdu 610031, China.

Yudong Wu (Y)

National Laboratory for Rail Transportation, Southwest Jiaotong University, Chengdu 610031, China.

Xiaguang Ren (X)

National Laboratory for Rail Transportation, Southwest Jiaotong University, Chengdu 610031, China.

Ting Zhao (T)

National Laboratory for Rail Transportation, Southwest Jiaotong University, Chengdu 610031, China.

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Classifications MeSH