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Implements two different lightweight architectures, CLFDNet and AELMFNet, combining multi-branch 1D CNNs and LSTM–attention for single and double fault-magnitude classification in multirotor UAVs, comparing six loss functions for efficient onboard health monitoring.
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najmulmowla1/Multirotor-UAV-Multi-Fault-Detection-with-Single-and-Double-Fault-Magnitudes
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Creative Commons Attribution 4.0 International Copyright (c) 2025 Md. Najmul Mowla This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. You are free to: - Share — copy and redistribute the material in any medium or format - Adapt — remix, transform, and build upon the material for any purpose, even commercially. Under the following terms: - Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
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Implements two different lightweight architectures, CLFDNet and AELMFNet, combining multi-branch 1D CNNs and LSTM–attention for single and double fault-magnitude classification in multirotor UAVs, comparing six loss functions for efficient onboard health monitoring.
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