ArticleScientific reports2024
UAV propeller fault diagnosis using deep learning of non-traditional χ
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Structural Vibration Analysis of UAVs Under Ground Engine Test Conditions.Sensors (Basel, Switzerland) · 2026Article
- Classification of time series using information granules for efficient detection of unmanned aerial vehicles faults.Scientific reports · 2025Article
- Self-attention enhanced GraphSAGE for UAV fault diagnosis using vibration signals.Scientific reports · 2025Article
- Reliability-oriented framework for UAV-based inspection missions in modern power and energy systems.Scientific reports · 2025Article
- Hybrid integral sliding mode and fuzzy logic control for omnidirectional robots: modified elephant herding optimization for trajectory tracking.Scientific reports · 2025Article
- Structure analysis in an octocopter using piezoelectric sensors and machine learning.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Fault detection and isolation in unmanned aerial vehicle (UAV) propellers are critical for operational safety and efficiency. Most existing fault diagnosis techniques rely basically on traditional statistical-based methods that necessitate better approaches. This study explores the application of untraditional feature extraction methodologies, namely Permutation Entropy (PE), Lempel-Ziv Complexity (LZC), and Teager-Kaiser Energy Operator (TKEO), on the PADRE dataset, which encapsulates various rotor fault configurations. The extracted features were subjected to a Chi-Square (χ
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.