Sound-Based Anomalies Detection in Agricultural Robotics Application

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Baltazar, AR (Autor) Outra; Filipe Neves Santos (Autor) FEUP; Soares, SP (Autor) Outra; António Paulo Moreira (Autor) FEUP; Cunha, JB (Autor) Outra

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2023, PT II

Páginas: 338-350

22nd EPIA Conference on Artificial Intelligence (EPIA)

Azores, PORTUGAL, SEP 05-08, 2023

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Z-KX8

DOI: 10.1007/978-3-031-49011-8_27

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13