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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/68620


    Title: Comparison of regression and neural network models for prediction of inspection profiles for aging aircraft
    Authors: Luxhoj, James T.;Williams, Trefor P.;徐煥智;Shyur, Huan-jyh
    Contributors: 淡江大學資訊管理學系
    Date: 1997-02-01
    Issue Date: 2011-10-23 13:19:50 (UTC+8)
    Abstract: Currently under phase 2 development by the Federal Aviation Administration (FAA), the Safety Performance Analysis System (SPAS) contains ‘alert’ indicators of aircraft safety performance that can signal potential problem areas for inspectors. The Service Difficulty Reporting (SDR) system is one component of SPAS and contains data related to the identification of abnormal, potentially unsafe conditions in aircraft and/or aircraft components/equipment.
    SPAS contains performance indicators to assist safety inspectors in diagnosing an airline's safety ‘profile’ compared with others in the same peer class. This paper details the development of SDR prediction models for the DC-9 aircraft by analyzing sample data from the SDR database that have been merged with aircraft utilization data. Both multiple regression and neural networks are used to create prediction models for the overall number of SDRs and for SDR cracking and corrosion cases. These prediction models establish a range for the number of SDRs outside which safety advisory warnings would be issued. It appears that a data ‘grouping’ strategy to create aircraft ‘profiles’ is very effective at enhancing the predictive accuracy of the models. The results from each competing modeling approach are compared and managerial implications to improve the SDR performance indicator in SPAS are provided.
    Relation: IIE transaction 29(2), pp.91-101
    DOI: 10.1080/07408179708966316
    Appears in Collections:[資訊管理學系暨研究所] 期刊論文

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