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Kathleen L Mcfadden – One of the best experts on this subject based on the ideXlab platform.
dwi convictions linked to a higher risk of alcohol related Aircraft AccidentsHuman Factors, 2002Co-Authors: Kathleen L McfaddenAbstract:
This paper assesses whether persons convicted of driving while intoxicated (DWI) are at increased risk of alcohol-related general aviation Accidents. Past research has shown a clear link between DWI convictions and pilot-error Accidents in commercial aviation. However, no study in the literature has addressed whether DWI convictions are associated with an increased risk of alcohol-related Aircraft Accidents. To evaluate a hypothesis, a total of 308 912 pilot records over a 10-year period were analyzed using logistic regression. After potentially confounding variables were controlled, DWI convictions were found to be associated with alcohol-related aviation Accidents. Pilots with DWI convictions were about 3.5 times more likely than pilots without convictions to have alcohol-related general aviation Accidents. Actual or potential applications of this research include providing policy makers with data-driven information that is useful in improving decisions related to the medical certification of pilots.
Matthew Dorn – One of the best experts on this subject based on the ideXlab platform.
Effects of Maintenance Human Factors in Maintenance-Related Aircraft AccidentsTransportation Research Record, 1996Co-Authors: Matthew DornAbstract:
To help prevent maintenance-related Aircraft Accidents the complex factor behind previous Accidents must be understood. Maintenance-related Aircraft accident were studied to determine the effects of maintenance human factors. A taxonomy of causal factors was developed and used to classify the causes of 101 military and civilian Accidents and to determine the frequency of occurence for each factor. The taxonomy identifies elements, such as people and hardware, interfaces between elements (i.e., human factors), and maintenance processes comprised of elements and interfaces. Human factors were found to have a significant effect in the 86 military and 15 civilian maintenancerelated Accidents studied. Whereas investigation boards were found to focus most heavily on element failures, a majority of the failures were found to occur at the process level. Maintenance instructions and their interfaces with the maintainers and inspectors who use them were the most frequently failed elements and interfaces, respectively. Recommendations are made to guide further research, and ideas are provided for improving process analysis by maintenance units and investigation boards.
Appavu Alias S Balamurugan – One of the best experts on this subject based on the ideXlab platform.
prediction of warning level in Aircraft Accidents using data mining techniquesAeronautical Journal, 2014Co-Authors: A Arockia B Christopher, Appavu Alias S BalamuruganAbstract:
Data mining is a data analysis process which is designed for large amounts of data. It proposes a methodology for evaluating risk and safety and describes the main issues of Aircraft Accidents. We have a huge amount of knowledge and data collection in aviation companies. This paper focuses on different feature selectwindion techniques applied to the datasets of airline databases to understand and clean the dataset. CFS subset evaluator, consistency subset evaluator, gain ratio feature evaluator, information gain attribute evaluator, OneR attribute evaluator, principal components attribute transformer, ReliefF attribute evaluatoboundar and symmetrical uncertainty attribute evaluator are used in this study in order to reduce the number of initial attributes. The classification algorithms, such as DT, KNN, SVM, NN and NB, are used to predict the warning level of the component as the class attribute. We have explored the use of different classification techniques on aviation components data. For this purpose Weka software tools are used. This study also proves that the principal components attribute with decision tree classifier would perform better than other attributes and techniques on airline data. Accuracy is also very highly improved. This work may be useful for an aviation company to make better predictions. Some safety recommendations are also addressed to airline companies.
feature selection techniques for prediction of warning level in Aircraft AccidentsInternational Conference on Advanced Computing, 2013Co-Authors: A Arockia B Christopher, Appavu Alias S BalamuruganAbstract:
This paper focuses on different feature selection techniques applied on the huge number of datasets of an airline databases to understand and clean the dataset. CFS Subset Evaluator, Consistency Subset Evaluator, Gain Ratio feature evaluator, Information Gain Attribute Evaluator, OneR feature evaluator, Principal Components Attribute Transformer (PCA), ReliefF Attribute Evaluator and Symmetrical Uncertainty Attribute Evaluator are used in this analysis in order to reduce the dataset. Also the Decision Tree classifier techniques of data mining are used to predict the warning level of the component as the class attribute in Aircraft Accidents for Risk and Safety. For this intention Weka software tools are used. This study also proved that the Principal Components Attribute Transformer would performance better than other attribute evaluators on airline data. This work may be useful for Aviation Company to make better prediction.