Failing And Not Falling (F&!F): Data-Enabled Classification Learning of Aircraft Accidents and Incidents

Abstract

Journey by aircraft is the only option for long-distance transportation and also one of the frequently used modes of transportation of passengers. As a result, safety of passengers and efficiency of the aircraft depend on maintaining efficient running conditions. Although many safety standards are followed in the design of the aircraft, and thus there are fewer accidents, it is necessary to perform a thorough analysis to avoid risks that may occur during flight time. In the present work, we propose a maintenance strategy, Failing And Not Falling (F&!F), based on the Federal Aviation Administration (FAA) data in the USA. We work with the dataset of Boeing 737. The data consists of 72 features with 137,236 records which describe an aircraft accident or incident. These features are used to predict whether an incident will be identified during aircraft maintenance or during aircraft operation and what specific type of incident will occur. The prediction method is based on the integration of a decision tree and a unique neural network at each node of the decision tree. The results obtained using different architectures show how deep the neural networks should be, how to identify the relevant features, and the success of combining decision trees and neural networks. Moreover, the neural networks and the decision tree approach also successfully identified the important features of maintenance. This method can be used for the maintenance of any data in multiple domains.

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Notes

  1. 1.

    https://www.faa.gov/data_research/aviation_data_statistics/data_downloads/

  2. 2.

    https://av-info.faa.gov/dd_sublevel.asp?Folder=%5CSDRS

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Correspondence to Aviv Segev.

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Carson, J., Hollingsworth, K., Datta, R. et al. Failing And Not Falling (F&!F): Data-Enabled Classification Learning of Aircraft Accidents and Incidents. Data-Enabled Discov. Appl. 4, 9 (2020). https://doi.org/10.1007/s41688-020-00044-0

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Keywords

  • Machine learning
  • Decision trees
  • Neural networks
  • Maintenance
  • Aircraft