Comparison of regression and neural network models for prediction of inspection profiles for aging aircraft

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.

[1]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

[2]  D. Rubinfeld,et al.  Econometric models and economic forecasts , 2002 .

[3]  James T. Luxhoj.  Sensitivity Analysis of Maintained Systems Using a Population Model: A Case Study , 1991 .

[4]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[5]  James T. Luxhoj,et al.  Reliability curve fitting for aging helicopter components , 1995 .

[6]  Kenneth W. Brammer.  A transient state maintenance requirements planning model , 1985 .

[7]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[8]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Trefor P. Williams,et al.  Using neural networks to predict component inspection requirements for aging aircraft , 1996 .

[10]  J. Hair.  Multivariate data analysis , 1972 .

[11]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[12]  James T. Luxhoj.  Importance Measures for System Components in Population Models , 1991 .

[13]  Marilyn S. Jones,et al.  A computerized population model for system repair/replacement , 1988 .

[14]  Frederick Mosteller,et al.  Understanding robust and exploratory data analysis , 1983 .

[15]  Maureen Caudill,et al.  Neural network training tips and techniques , 1991 .

[16]  S Mangrulkar,et al.  Artificial neural systems. , 1990, ISA transactions.

[17]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[18]  David E. Rumelhart,et al.  Predicting the Future: a Connectionist Approach , 1990, Int. J. Neural Syst..

[19]  James T. Luxhoj.  Replacement analysis for components of large scale production systems , 1992 .