Abstract :

Permanent Magnet Synchronous Machines (PMSMs) Are Widely Used In Industrial Applications Due To Their High Efficiency And Precise Control Capabilities; However, Stator Faults Remain A Critical Threat To Their Operational Reliability. Traditional Detection Methods, Such As Periodic Inspections And Basic Electrical Monitoring, Often Fall Short In Providing Early, Accurate Fault Detection And Can Result In Either False Alarms Or Overlooked Issues. These Limitations Contribute To Unplanned Downtime, Increased Maintenance Costs, And Potential Equipment Damage. To Address This Challenge, The Proposed System Integrates Advanced Machine Learning Algorithms With Sensor Fusion Techniques To Improve The Accuracy And Reliability Of Stator Fault Detection In PMSMs. By Leveraging Data From Multiple Sensors—such As Voltage, Current, Temperature, And Vibration—the System Offers A Holistic View Of Machine Health. Trained On Historical Datasets, The Machine Learning Models Identify Patterns Linked To Stator Faults, While Built-in False Alarm Suppression Algorithms Ensure Only Genuine Alerts Prompt Maintenance Action. This Approach Enables Proactive Maintenance, Reduces Downtime, Enhances Safety, And Lowers Operational Costs. Keywords: PMSM, Stator Fault Detection, Machine Learning, Sensor Fusion, Predictive Maintenance

Published:

09-6-2025

Issue:

Vol. 25 No. 6 (2025)

Page Nos:

124-136

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite