Fault Detection in Induction Motors using Functional Dimensionality
  Reduction Methods
María Barroso José M. Bossio Carlos M. Alaíz Ángela Fernández
The implementation of strategies for fault detection and diagnosis on
rotating electrical machines is crucial for the reliability and safety of
modern industrial systems. The contribution of this work is a methodology that
combines conventional strategy of Motor Current Signature Analysis with
functional dimensionality reduction methods, namely Functional Principal
Components Analysis and Functional Diffusion Maps, for detecting and
classifying fault conditions in induction motors. The results obtained from the
proposed scheme are very encouraging, revealing a potential use in the future
not only for real-time detection of the presence of a fault in an induction
motor, but also in the identification of a greater number of types of faults
present through an offline analysis.