Affiliations: [a] Department of Industrial Engineering and Management, SCE – Shamoon College of Engineering, Beer-Sheva, Israel | [b] Department of Industrial Engineering and Management, HIT – Holon Institute of Technology, Holon, Israel
Correspondence:
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Corresponding author: Dima Alberg, Department of Industrial Engineering and Management, SCE – Shamoon College of Engineering, Beer-Sheva, Israel. E-mail: dimitria@sce.ac.il.
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 result increasing the overall level of aircraft safety. Several machine learning techniquesexist to learn from big amounts of data. However, the use of thesetechniques to infer the desired readable and accurate interval regression tree models from the data obtained during theoperation and maintenance of aircraft is extremely challenging. Difficulties that need to be addressed include: data warehouse collection and preprocessing, data labeling, machine learning model readability, setup, evaluation and maintenance. This paper presents the Interval Gradient Prediction Tree algorithm INGPRET, which addresses these issues. As shown by our empirical evaluation of a real aircraft multi-sensor data set, the INGPRET algorithm provides better readability and similar performance in comparison to other regression tree machine learning algorithms.
Keywords: Multi-sensor data, aircraft maintenance, data streams interval prediction, interval regression tree
