# Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods

arXiv: https://arxiv.org/abs/2306.09365 · HTML: https://mlsanity.com/paper/2306.09365/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery](https://mlsanity.com/paper/2102.11848/) (2021)
- [Benchmarking time series classification -- Functional data vs machine learning approaches](https://mlsanity.com/paper/1911.07511/) (2019)
- [Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction](https://mlsanity.com/paper/2012.11987/) (2020)

## Built on by (most cited first, 1 of 1)

- [Fault Diagnosis on Induction Motor using Machine Learning and Signal Processing](https://mlsanity.com/paper/2401.15417/) (2024)

## Abstract (arXiv)

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.