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Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard Ph.D., Austin T. Walden Ph.D., Paul J. Thomas Ph.D.
Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard Ph.D., Austin T. Walden Ph.D., Paul J. Thomas Ph.D.
Journal of Aviation/Aerospace Education & Research
Increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations: 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) developing an embedded machine learning framework. Data cleanup and …
Business Inferences And Risk Modeling With Machine Learning; The Case Of Aviation Incidents, Burak Cankaya, Kazim Topuz, Aaron M. Glassman
Business Inferences And Risk Modeling With Machine Learning; The Case Of Aviation Incidents, Burak Cankaya, Kazim Topuz, Aaron M. Glassman
Publications
Machine learning becomes truly valuable only when decision-makers begin to depend on it to optimize decisions. Instilling trust in machine learning is critical for businesses in their efforts to interpret and get insights into data, and to make their analytical choices accessible and subject to accountability. In the field of aviation, the innovative application of machine learning and analytics can facilitate an understanding of the risk of accidents and other incidents. These occur infrequently, generally in an irregular, unpredictable manner, and cause significant disruptions, and hence, they are classified as "high-impact, low-probability" (HILP) events. Aviation incident reports are inspected by …
Identification Of Reverse Engineering Candidates Utilizing Machine Learning And Aircraft Cannibalization Data, Marc Banghart
Identification Of Reverse Engineering Candidates Utilizing Machine Learning And Aircraft Cannibalization Data, Marc Banghart
International Journal of Aviation, Aeronautics, and Aerospace
As military aircraft continue to remain in service and age, cannibalization of parts is increasing. Proactive identification of parts that are at high risk for cannibalization will inform engineering processes such as reverse engineering, thus allowing potentially reducing lead time to develop new parts. The research objective was to develop a causal structure that can be used for prediction of when cannibalization actions may occur. Bayesian networks allow encoding of causality between various descriptive features given a data set. The method utilized a tabu search algorithm, identified the underlying causal structure and the associated node probabilities. The method is then …