Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods

2023/06/14 by María Barroso, José M. Bossio, Barroso, María +5
Engineering · #Artificial Intelligence (cs.AI) #Engineering Diagnostics and Reliability #FOS: Computer and information sciences #FOS: Electrical engineering #Fault Detection and Control Systems #Machine Fault Diagnosis Techniques #Machine Learning (cs.LG) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2306.09365

openalex publication_date 2023/06/14 · openalex created_date 2023/06/20 · openalex updated_date 2026/07/28

Abstract

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.

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