The wide applications of automatic sensing devices and data acquisition systems in automotive manufacturing have resulted in a data-rich environment, which demands new data mining methodologies for effective data fusion and information integration to support decision making. This paper presents a new methodology for developing a diagnostic system using manufacturing system data for high-value assets in automotive manufacturing. The proposed method extends the basic attributes control charts with the following key elements: optimal feature subset selection considering multiple features and correlation structure, balancing the type I and type II errors in decision making, on-line process monitoring using adaptive modeling with control charts, and diagnostic performance assessment using shift and trend detection. The performance of the developed diagnostic system can be continuously improved as the knowledge of machine faults is automatically accumulated during production. An example of the analysis at one Ford production plant is provided to demonstrate the implementation of this methodology.


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    Title :

    A Data-Driven Diagnostic System Utilizing Manufacturing Data Mining and Analytics


    Additional title:

    Sae Int. J. Mater. Manf
    Sae International Journal of Materials and Manufacturing


    Contributors:
    Januszczak, Annette (author) / Wang, Hui (author) / Suriano, Saumuy (author) / Guo, Shenghan (author) / Guo, Weihong (author) / Yu, Xiao (author)

    Conference:

    WCX™ 17: SAE World Congress Experience ; 2017



    Publication date :

    2017-03-28


    Size :

    11 pages




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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