Aircraft Maintenance Prediction Tree Algorithms

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Author(s): Dima Alberg (Shamoon College of Engineering, Israel)and Yossi Hadad (Shamoon College of Engineering, Israel)
Copyright: 2023
Pages: 13
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch120

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

The operation and maintenance of modern aircraft multi-sensor data fusion systems generate vast amounts of numerical and symbolic data. Learning useful and non-trivial insights from this data may lead to considerable savings, and detection and reduction of the number of faults, as a result increasing the overall level of aircraft safety. Several machine learning techniques exist to learn from big amounts of data. However, the use of these techniques to infer the desired readable and accurate interval regression tree models from the data obtained during the operation and maintenance of aircraft is extremely challenging. Difficulties that need to be addressed include data warehouse collection and preprocessing, machine learning model readability, setup, evaluation, and maintenance. This article presents the interval gradient prediction tree algorithm (INGPRET), which addresses these issues. As shown by the empirical evaluation of a real aircraft multi-sensor data set, the INGPRET algorithm provides better readability and similar performance in comparison to other machine learning algorithms.

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