Automatic anomaly detection in vibration analysis based on Machine Learning Algorithms

• Autor(a) principal: Torres, Pedro
• Data de Publicação: 2022
• Outros Autores: Correia, Luis, Ramalho, Armando
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.11/8113
• topic: Industry 4.0; Anomaly detection; Isolation forest; Vibration analysis; BigML

This paper presents an approach for automatic anomaly detection through vibration analysis based on machine learning algorithms.The study focuses on induction motors in a predictive maintenance context, but can be applied to other domains. Vibration analysis is an important diagnostic tool in industrial data analysis to predict anomaliescaused by equipment defects or in its use, allowing to increase its lifetime.It is not a new technique and is widely used in the industry, however withthe Industry 4.0 paradigm and the need to digitize any process, it gainsrelevance to automatic fault detection. The Isolation Forest algorithm isimplemented to detect anomalies in vibration datasets measured in anexperimental apparatus composed of an induction motor and a coupling system with shaft alignment/misalignment capabilities. The results showthat it is possible to detect anomalies automatically with a high level ofprecision and accuracy.