Automated Fault Detection in Quadrocopter Propellers Using AI and Acoustic Data

Automated Fault Detection in Quadrocopter Propellers Using AI and Acoustic Data | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 March 2025 V1 Latest version Share on Automated Fault Detection in Quadrocopter Propellers Using AI and Acoustic Data Author : John Olusegun Fajinmi 0009-0008-1648-9572 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174231156.69448902/v1 267 views 123 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The rapid proliferation of quadcopters in various applications, from surveillance to delivery services, has necessitated the development of efficient fault detection systems to ensure operational safety and reliability. This study explores the use of Artificial Intelligence (AI) and acoustic data for automated fault detection in quadcopter propellers. By leveraging machine learning algorithms, the proposed system analyzes acoustic signatures generated by propellers during operation to identify anomalies such as cracks, imbalances, or deformations. A dataset of acoustic signals was collected from both healthy and faulty propellers under varying operational conditions. Feature extraction techniques, including spectral analysis and time-frequency transformations, were employed to preprocess the data. The processed data was then used to train and validate AI models, including Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). Experimental results demonstrate the effectiveness of the proposed approach, achieving high accuracy in fault classification and early detection. This research highlights the potential of AIdriven acoustic analysis as a non-invasive, cost-effective solution for real-time fault detection in quadcopter propellers, enhancing maintenance efficiency and reducing downtime. Supplementary Material File (automated fault detection in quadrocopter propellers using ai and acoustic data.pdf) Download 194.74 KB Information & Authors Information Version history V1 Version 1 18 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords acoustic data analysis anomaly detection artificial intelligence automated fault detection convolutional neural networks machine learning Authors Affiliations John Olusegun Fajinmi 0009-0008-1648-9572 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 267 views 123 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation John Olusegun Fajinmi. Automated Fault Detection in Quadrocopter Propellers Using AI and Acoustic Data. Authorea . 18 March 2025. DOI: https://doi.org/10.22541/au.174231156.69448902/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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