Version 1
: Received: 22 May 2024 / Approved: 23 May 2024 / Online: 24 May 2024 (09:32:42 CEST)
How to cite:
Jeong, S. H.; Kang, D.; Lee, I.; Lee, Y.; Kim, J. H.; Hwang, Y.-Y. Gap Filling of Missing and Outlier Values of Rotorcraft Flight Data using Multilayer Perceptron. Preprints2024, 2024051581. https://doi.org/10.20944/preprints202405.1581.v1
Jeong, S. H.; Kang, D.; Lee, I.; Lee, Y.; Kim, J. H.; Hwang, Y.-Y. Gap Filling of Missing and Outlier Values of Rotorcraft Flight Data using Multilayer Perceptron. Preprints 2024, 2024051581. https://doi.org/10.20944/preprints202405.1581.v1
Jeong, S. H.; Kang, D.; Lee, I.; Lee, Y.; Kim, J. H.; Hwang, Y.-Y. Gap Filling of Missing and Outlier Values of Rotorcraft Flight Data using Multilayer Perceptron. Preprints2024, 2024051581. https://doi.org/10.20944/preprints202405.1581.v1
APA Style
Jeong, S. H., Kang, D., Lee, I., Lee, Y., Kim, J. H., & Hwang, Y. Y. (2024). Gap Filling of Missing and Outlier Values of Rotorcraft Flight Data using Multilayer Perceptron. Preprints. https://doi.org/10.20944/preprints202405.1581.v1
Chicago/Turabian Style
Jeong, S. H., Jeong Ho Kim and Yun-Young Hwang. 2024 "Gap Filling of Missing and Outlier Values of Rotorcraft Flight Data using Multilayer Perceptron" Preprints. https://doi.org/10.20944/preprints202405.1581.v1
Abstract
Rotorcrafts operate HUMS for fault diagnosis, but the capabilities of HUMS analysts are important due to the complex mechanical configuration of rotorcraft and the difficulty of analysis. Accordingly, an advanced algorithm is needed to identify faults, and flight data can be used to develop the algorithm. However, flight data contains abnormal or missing data due to problems such as sensor defects, which can be a major problem in algorithm development. Therefore, in this paper, we study a gap filling method using MLP to obtain normal data when there are problems such as abnormal or missing data in rotorcraft flight data. Existing methods typically fill the gap through interpolation or curve fitting using the before and after values of missing data. However, in this study, normal values were estimated using the values of other parameters at the moment of missing or abnormal data occurred. As a result of evaluating the performance of the trained MLP using normal data, it was confirmed that the output of the model well estimates the changing trend of the target. And when abnormal data was applied to the trained model, it was confirmed that point outliers were eliminated and appropriate values were found in the case of missing data.
Keywords
rotorcraft; flight data; gap filling; multilayer perceptron; health and usage monitoring system
Subject
Engineering, Aerospace Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
