Research on Condition Recognition Method Based on DK‑SVDD for In‑wheel Motor Bearing

被引：0
|
作者：
Li Z. [1 ]
Xi S. [1 ]
Xue H. [1 ]
Liu B. [1 ]
Zhu F. [1 ]
机构：
[1] School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang
来源：
Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2023年 / 43卷 / 06期
关键词：
condition recognition; double kernel; double kernel based support vector data description; in-wheel motor bearing; support vector data description;
D O I：
10.16450/j.cnki.issn.1004-6801.2023.06.011
中图分类号：
学科分类号：
摘要：
In order to further improve the efficiency and reliability of electric vehicle in-wheel motor bearing condition recognition technology，a condition recognition method based on double kernel based support vector data description（DK-SVDD）is proposed. Aiming at the lower recognition rate of SVDD caused by the mixed data structure，the DK kernel is constructed by combining the radial basis function（RBF）kernel function and the difference of Gaussians（DOG）kernel function with a certain proportion weight. According to the optimal binary tree principle，the condition recognition classifier is designed layer by layer，and the DK-SVDD in-wheel motor bearing condition recognition model is built. At the same time，the particle swarm optimization algorithm is used to optimize the model parameters to improve the learning ability and generalization ability of DK-SVDD. Based on the bench test data of in-wheel motor bearing，the feasibility of the proposed method is verified. The results show that the average training time of DK-SVDD method is 0.065 5 s and the average condition recognition rate is 97.06%. Secondly，compared with RBF or DOG kernel function，DK-SVDD method can effectively improve the condition recognition rate and reduce the training time under various working conditions. According to the above results，the validity and superiority of proposed method based on DK-SVDD are verified. Obtained results can provide reference for the subsequent development of in-wheel motor bearing state identification to improve the safety and reliability of electric vehicles. © 2023 Nanjing University of Aeronautics an Astronautics. All rights reserved.
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页码：1121 / 1128and1243and1244
相关论文
共 19 条
  • [1] LI Zhongxing, QIN Xia, XUE Hongtao, A in-wheel motor fault diagnosis method based on BN and improved DST［J］, Journal of Huazhong University of Science and Technology（Natural Science Edition）, 49, 8, pp. 27-32, (2021)
  • [2] DING Dianyong, XUE Hongtao, LIU Bingchen, An OSSD-EMOMEDA based fault feature extraction method for in-wheel motor bearings［J/OL］, Proceedings of the CSEE
  • [3] WANG Yujing, LI Shaopeng, KANG Shouqiang, Et al., Prediction method of residual service life of rolling bearing based on CNN and LSTM［J］, Journal of Vibration，Measurement & Diagnosis, 41, 3, pp. 439-446, (2021)
  • [4] XU Renyi, WANG Hang, PENG Minjun, Et al., New method for residual life prediction and its application in rolling bearings［J］, Journal of Vibration，Measurement & Diagnosis, 42, 4, pp. 636-643, (2022)
  • [5] XUE Hongtao, TONG Peng, JIANG Hong, A fuzzy diagnosis method based on RS and AHNs for faults of in-wheel motor［J］, Journal of Vibration，Measurement & Diagnosis, 42, 5, pp. 925-930, (2022)
  • [6] QIU S Q，, CHEN X，, Et al., A hyper-ellipsoidal support vector data description incremental learning method for bearing fault diagnosis［C］//2020 Global Reliability and Prognostics and Health Management （PHM-Shanghai）, pp. 1-6, (2020)
  • [7] JIAO Jing, YUE Jianhai, PEI Di, A rolling bearing fault diagnosis method based on MSK-SVM［J］, Journal of Electronic Measurement and Instrument, 36, 1, pp. 109-117, (2022)
  • [8] LIAO Aihua, WU Yilan, DING Yaqi, Performance degradation assessment of rolling stock bearings in combination with PSO-OEWOA and MKSVDD［J］, Journal of Railway Science and Engineering, 19, 9, pp. 2730-2738, (2022)
  • [9] ZHANG Xunjie, YUAN Yi, LI Xianjun, Et al., Fault diagnosis of rolling bearing based on CF extraction and MBA-SVDD［J］, Machine Tool & Hydraulics, 50, 1, pp. 182-188, (2022)
  • [10] XUE H T, ZHANG Z M，, Et al., Intelligent diagnosis of mechanical faults of in-wheel motor based on improved artificial hydrocarbon networks［J］, ISA Transactions, 120, pp. 360-371, (2022)
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