Work

Machine Learning‐Based Detection of Broken Bars in Dual Three‐Phase Induction Motors: Methodology and Experimental Validation

Year: 2025

Type: article

Abstract: ABSTRACT Dual three‐phase induction motor drives are gaining attention in applications where reliability is a critical concern, such as automotive. Although the dual‐stator winding reduces the impact of stator phase failures, these motors remain susceptible to broken rotor bar (BRB) faults. This paper presents a comprehensive methodology for their detection. The proposed approach encompasses all the key components of the detection algorithm, from motor modelling—used to generate a virtual dataset for training—to the selection of the most suitable variables for machine learning classification. Technical challenges, practical implementation insights, and experimental validation on real motors are discussed to provide a thorough understanding of the methodology and best design practices.

Source: IET Electric Power Applications

DOI: 10.1049/elp2.70069

Authors: Riccardo Antonello, Marco Pastura, Mauro Zigliotto

Institution: University of Padua

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,880