Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods
This work addresses reliability and safety issues in industrial systems by improving fault detection for induction motors, but it is incremental as it builds on existing methods.
The paper tackled fault detection in induction motors by combining Motor Current Signature Analysis with functional dimensionality reduction methods, achieving encouraging results for real-time detection and classification of multiple fault types.
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.