Due to the complex working environment and lack of network management protocol, it is difficult to comprehensively monitor the operating states and diagnose the faults of the Multifunction Vehicle Bus (MVB). In this paper, an MVB fault diagnostic method based on physical waveform features and ensemble pruning has been proposed. Firstly, MVB waveforms of the bus administrator node (BA) in normal and fault conditions are sampled by an MVB analyzer based on high-speed A/D sampling technology. Network features are extracted from the waveforms, and a random forest (RF) classifier has been trained to classify different MVB faults. An ensemble pruning method based on diversity index and the k-mean algorithm has been proposed to reduce the number of decision trees and improve the ensemble performance. The experimental results show that the proposed feature extraction method and ensemble pruning classifier can recognize the MVB faults correctly, reduce the number of base classifiers, and improve the accuracy of the subforest compared with the original RF ensemble.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Fault Diagnosis of MVB Based on Random Forest and Ensemble Pruning


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Qin, Yong (editor) / Jia, Limin (editor) / Liu, Baoming (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / Li, Zhaozhao (author) / Wang, Lide (author) / Shen, Ping (author) / Song, Hui (author)

    Conference:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2019 ; Qingdao, China October 25, 2019 - October 27, 2019



    Publication date :

    2020-04-04


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Fault Diagnosis of MVB Based on Random Forest and Ensemble Pruning

    Li, Zhaozhao / Wang, Lide / Shen, Ping et al. | British Library Conference Proceedings | 2020


    Fault Diagnosis of MVB Based on Random Forest and Ensemble Pruning

    Li, Zhaozhao / Wang, Lide / Shen, Ping et al. | TIBKAT | 2020



    Satellite attitude control system fault diagnosis and early warning method based on random forest

    ZHONG MAIYING / HUANG JIN / HE KAIXUN et al. | European Patent Office | 2020

    Free access

    Aircraft environment control system air cooling equipment robust fault diagnosis method based on random forest

    TAO LAIFA / CHEN YU / ZHANG XINGLIU et al. | European Patent Office | 2020

    Free access