Sound-Based Anomalies Detection in Agricultural Robotics Application
Authors: 
André Rodrigues Baltazar, Filipe Neves dos Santos, Salviano Pinto Soares, António Paulo Moreira, José Boaventura CunhaAuthors Info & Claims
Pages 338 - 350
Abstract
Agricultural robots are exposed to adverse conditions reducing the components’ lifetime. To reduce the number of inspection, repair and maintenance activities, we propose using audio-based systems to diagnose and detect anomalies in these robots. Audio-based systems are non-destructive/intrusive solutions. Besides, it provides a significant amount of data to diagnose problems and for a wiser scheduler for preventive activities. So, in this work, we installed two microphones in an agricultural robot with a mowing tool. Real audio data was collected with the robotic mowing tool operating in several conditions and stages. Besides, a Sound-based Anomalies Detector (SAD) is proposed and tested with this dataset. The SAD considers a short-time Fourier transform (STFT) computation stage connected to a Support Vector Machine (SVM) classifier. The results with the collected dataset showed an F1 score between 95% and 100% in detecting anomalies in a mowing robot operation.
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Information
Published In
Sep 2023
605 pages
ISBN:978-3-031-49010-1
DOI:10.1007/978-3-031-49011-8
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 15 December 2023
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