What question did this study set out to answer?

This research aims to improve fault detection in induction motors by optimizing current signal preprocessing techniques for machine learning models.

March 16, 2026IET Electric Power Applications0 citationsOpen Access

Induction Motor Multifault Detection Using Machine Learning and Signal Preprocessing

Induction Motor Multifault Detection Using Machine Learning: Improving Model Accuracy With Current Signal Preprocessing

View Full Paper

Authors

SKSemen Koveshnikov NBNada El Bouharrouti ASAlireza Saberi

Discussion

Member takes

Overview

Exploratory analysis demonstrates improved fault detection in motors using machine learning, suggesting enhanced methods for industrial applications.

Key Points

This research aims to improve fault detection in induction motors by optimizing current signal preprocessing techniques for machine learning models.
Investigated various parameters and techniques for current signal acquisition and preprocessing.
Evaluated a multilayer perceptron model for fault detection.
Applied signal multirate resampling for enhanced algorithm accuracy.
Utilized scatter plots for feature introspection in machine learning.
Improvements in prediction accuracy for detecting faults through optimized preprocessing methods.
Highlighted the challenges of achieving higher frequency resolution in real-world scenarios.
Demonstrated potential refinements in system performance for industrial applications.

Ask AI

Helpful

Bookmark

View Full Paper

Cite This Study

Koveshnikov et al. (2026) studied this question.

synapsesocial.com/papers/69b79e968166e15b153ac2c8 https://doi.org/10.1049/elp2.70160

Ask AI

Helpful

Bookmark

View Full Paper

March 16, 2026IET Electric Power Applications0 citationsOpen Access

Induction Motor Multifault Detection Using Machine Learning and Signal Preprocessing

Induction Motor Multifault Detection Using Machine Learning: Improving Model Accuracy With Current Signal Preprocessing

View Full Paper

Authors

SKSemen Koveshnikov NBNada El Bouharrouti ASAlireza Saberi

Discussion

Member takes

Overview

Exploratory analysis demonstrates improved fault detection in motors using machine learning, suggesting enhanced methods for industrial applications.

Key Points

This research aims to improve fault detection in induction motors by optimizing current signal preprocessing techniques for machine learning models.
Investigated various parameters and techniques for current signal acquisition and preprocessing.
Evaluated a multilayer perceptron model for fault detection.
Applied signal multirate resampling for enhanced algorithm accuracy.
Utilized scatter plots for feature introspection in machine learning.
Improvements in prediction accuracy for detecting faults through optimized preprocessing methods.
Highlighted the challenges of achieving higher frequency resolution in real-world scenarios.
Demonstrated potential refinements in system performance for industrial applications.

Ask AI

Helpful

Bookmark

View Full Paper

Cite This Study

Koveshnikov et al. (2026) studied this question.

synapsesocial.com/papers/69b79e968166e15b153ac2c8 https://doi.org/10.1049/elp2.70160

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

Ask AI

Helpful

Bookmark

View Full Paper