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 anomalies caused 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 with the Industry 4.0 paradigm and the need to digitize any process, it gains relevance to automatic fault detection. The Isolation Forest algorithm is implemented to detect anomalies in vibration datasets measured in an experimental apparatus composed of an induction motor and a coupling system with shaft alignment/misalignment capabilities. The results show that it is possible to detect anomalies automatically with a high level of precision and accuracy.


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    Titel :

    Automatic Anomaly Detection in Vibration Analysis Based on Machine Learning Algorithms


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Machado, José (Herausgeber:in) / Soares, Filomena (Herausgeber:in) / Trojanowska, Justyna (Herausgeber:in) / Yildirim, Sahin (Herausgeber:in) / Vojtěšek, Jiří (Herausgeber:in) / Rea, Pierluigi (Herausgeber:in) / Gramescu, Bogdan (Herausgeber:in) / Hrybiuk, Olena O. (Herausgeber:in) / Torres, Pedro (Autor:in) / Ramalho, Armando (Autor:in)

    Kongress:

    International Conference Innovation in Engineering ; 2022 ; Minho, Portugal June 28, 2022 - June 30, 2022



    Erscheinungsdatum :

    2022-06-21


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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