YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASME
    • Journal of Dynamic Systems, Measurement, and Control
    • View Item
    •   YE&T Library
    • ASME
    • Journal of Dynamic Systems, Measurement, and Control
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Fault Detection of Automotive Engine System Based on Canonical Variate Analysis Combined With Bhattacharyya Distance

    Source: Journal of Dynamic Systems, Measurement, and Control:;2024:;volume( 147 ):;issue: 004::page 41004-1
    Author:
    Cheng, Zhang
    ,
    Yun-Fei, Wang
    ,
    Yu-Yu, Lao
    ,
    Yuan, Li
    DOI: 10.1115/1.4067262
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Aiming at the nonlinear and dynamic characteristics of data in automotive engine systems, a fault detection method based on canonical variate analysis combined with Bhattacharyya distance (CVA-BD) is proposed in this paper. First, CVA is utilized to calculate the state space of the system data. Second, a sliding window is introduced in the state space to quantify the difference in data distribution within the window using Bhattacharyya distance, thereby constructing a novel statistical indicator. Finally, the control limit for statistical indicator is determined to achieve process monitoring of automotive engine systems. CVA-BD effectively enhances the performance of process monitoring by capturing the sequential correlation of data through CVA and eliminating the nonlinear impact between samples using similarity measurement metrics. Simulation experiments are conducted using a numerical case and experimental data from turbocharged spark ignition (TCSI) engines. The simulation results further confirm that, compared with principal component analysis (PCA), dynamic principal component analysis (DPCA), canonical variable analysis (CVA), dissimilar canonical variable analysis (CVDA), auto-encoder (AE), and stacked auto-encoder (SAE) CVA-BD has demonstrated an improvement of at least 41%.
    • Download: (2.049Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Fault Detection of Automotive Engine System Based on Canonical Variate Analysis Combined With Bhattacharyya Distance

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4306053
    Collections
    • Journal of Dynamic Systems, Measurement, and Control

    Show full item record

    contributor authorCheng, Zhang
    contributor authorYun-Fei, Wang
    contributor authorYu-Yu, Lao
    contributor authorYuan, Li
    date accessioned2025-04-21T10:22:32Z
    date available2025-04-21T10:22:32Z
    date copyright12/23/2024 12:00:00 AM
    date issued2024
    identifier issn0022-0434
    identifier otherds_147_04_041004.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306053
    description abstractAiming at the nonlinear and dynamic characteristics of data in automotive engine systems, a fault detection method based on canonical variate analysis combined with Bhattacharyya distance (CVA-BD) is proposed in this paper. First, CVA is utilized to calculate the state space of the system data. Second, a sliding window is introduced in the state space to quantify the difference in data distribution within the window using Bhattacharyya distance, thereby constructing a novel statistical indicator. Finally, the control limit for statistical indicator is determined to achieve process monitoring of automotive engine systems. CVA-BD effectively enhances the performance of process monitoring by capturing the sequential correlation of data through CVA and eliminating the nonlinear impact between samples using similarity measurement metrics. Simulation experiments are conducted using a numerical case and experimental data from turbocharged spark ignition (TCSI) engines. The simulation results further confirm that, compared with principal component analysis (PCA), dynamic principal component analysis (DPCA), canonical variable analysis (CVA), dissimilar canonical variable analysis (CVDA), auto-encoder (AE), and stacked auto-encoder (SAE) CVA-BD has demonstrated an improvement of at least 41%.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleFault Detection of Automotive Engine System Based on Canonical Variate Analysis Combined With Bhattacharyya Distance
    typeJournal Paper
    journal volume147
    journal issue4
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.4067262
    journal fristpage41004-1
    journal lastpage41004-10
    page10
    treeJournal of Dynamic Systems, Measurement, and Control:;2024:;volume( 147 ):;issue: 004
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian