期刊論文
| 學年 | 85 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 1997-02-01 |
| 作品名稱 | Comparison of regression and neural network models for prediction of inspection profiles for aging aircraft |
| 作品名稱（其他語言） | |
| 著者 | Luxhoj, James T. ; Williams, Trefor P.; 徐煥智; Shyur, Huan-jyh |
| 單位 | 淡江大學資訊管理學系 |
| 出版者 | |
| 著錄名稱、卷期、頁數 | IIE transaction 29(2), pp.91-101 |
| 摘要 | 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. |
| 關鍵字 | |
| 語言 | en |
| ISSN | |
| 期刊性質 | 國內 |
| 收錄於 | SCI EI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/68620 ) |