Version 1
: Received: 22 May 2024 / Approved: 23 May 2024 / Online: 23 May 2024 (07:35:06 CEST)
How to cite:
Kang, D.; Jeong, S. H.; Lee, I.; Lee, Y.; Shim, B.; Hwang, Y.-Y. Enhancing Predictive Maintenance in Airborne Weapon Systems through Advanced Frequency Augmentation Techniques. Preprints2024, 2024051515. https://doi.org/10.20944/preprints202405.1515.v1
Kang, D.; Jeong, S. H.; Lee, I.; Lee, Y.; Shim, B.; Hwang, Y.-Y. Enhancing Predictive Maintenance in Airborne Weapon Systems through Advanced Frequency Augmentation Techniques. Preprints 2024, 2024051515. https://doi.org/10.20944/preprints202405.1515.v1
Kang, D.; Jeong, S. H.; Lee, I.; Lee, Y.; Shim, B.; Hwang, Y.-Y. Enhancing Predictive Maintenance in Airborne Weapon Systems through Advanced Frequency Augmentation Techniques. Preprints2024, 2024051515. https://doi.org/10.20944/preprints202405.1515.v1
APA Style
Kang, D., Jeong, S. H., Lee, I., Lee, Y., Shim, B., & Hwang, Y. Y. (2024). Enhancing Predictive Maintenance in Airborne Weapon Systems through Advanced Frequency Augmentation Techniques. Preprints. https://doi.org/10.20944/preprints202405.1515.v1
Chicago/Turabian Style
Kang, D., Byoungserb Shim and Yun-Young Hwang. 2024 "Enhancing Predictive Maintenance in Airborne Weapon Systems through Advanced Frequency Augmentation Techniques" Preprints. https://doi.org/10.20944/preprints202405.1515.v1
Abstract
This study investigates the application of frequency augmentation techniques to improve condition-based maintenance (CBM+) systems for airborne weapon systems, focusing on the predictive accuracy of velocity and acceleration parameters. Utilizing MIL-STD-1553B MUX data from 138 sorties of fixed-wing aircraft, we explored six augmentation methods, including traditional interpolations (linear, quadratic, cubic spline) and advanced machine learning models (K-Nearest Neighbor, LSTM, and Bi-LSTM). Our findings indicate that while traditional methods like linear interpolation are more effective for velocity parameters, advanced ML techniques, particularly Bi-LSTM, provide better results for acceleration parameters, which exhibit more complex and rapid variability. The study underscores the necessity of tailoring augmentation approaches to specific parameter characteristics and highlights the potential of ML models in capturing intricate patterns within time-series data. These insights are critical for advancing CBM+ systems in military avionics, enhancing reliability and operational efficiency through improved data processing strategies. Future work should explore more performant augmentation techniques, such as by integrating multi-dimensional datasets, and explore ways to develop automated tools for a more powerful predictive maintenance framework.
Keywords
airborne weapon system; condition based maintenance; augmentation; LSTM; bidirectional LSTM
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.
