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Application of artificial intelligence techniques to the study of machine signatures

Abstract:

This paper presents demonstration on the application of artificial intelligence techniques to the study of machine vibration signatures. First, a Self-Organizing Map (SOM) is used to discover cluster information from frequency-domain vibration signatures for the detection and diagnosis of unbalanced rotor and bearing faults. In the next, with further feature extraction in frequency-domain, a 2-dimensional multi-class Support Vector Machine (SVM) is used to pbkp_redict these fault modes with an error rate of 1.48% over a wide machine operation speed. © 2012 IEEE.

Año de publicación:

2012

Keywords:

    Fuente:

    scopusscopus

    Tipo de documento:

    Conference Object

    Estado:

    Acceso restringido

    Áreas de conocimiento:

    • Aprendizaje automático
    • Ciencias de la computación

    Áreas temáticas:

    • Métodos informáticos especiales

    Contribuidores:

    Sanjib Kumar PandaSanjib Kumar Panda
    Xu J.X.Xu J.X.
    Chen W.Y.Chen W.Y.