An Intelligent Hybrid Feature Selection Approach for SCIM Inter-Turn Fault Classification at Minor Load Conditions Using Supervised Learning

Abstract

In industries, squirrel cage induction motors are crucial for supplying rotary motion in power tools. This research presents a robust but simple framework for an inter-turn fault classification at minor loading across diverse fault occurrence conditions, which is one of the most common defects in a squirrel cage induction motor. Early detection of this issue is critical to prevent the system from completely failing as a result of it evolving to a more severe stator winding fault. This study employs a hybrid feature selection strategy (a hybrid of a filter-based and a wrapper-based approach) using the Hilbert Transform signal processing technique and a statistical feature extraction approach, which is then fed to a support vector machine as the classifier. The suggested framework is tested and validated against other known classifier models. The results demonstrate that the model has a computationally low diagnostic performance process with exceptional accuracy. Furthermore, when compared to there classifier models, the suggested model provided the best diagnostic outcome on the stator winding fault classification, demonstrating its dependability in fault diagnostic classification for squirrel cage induction motors.

Keywords

• Diagnostics
• squirrel cage induction motor
• fault classification
• turn-to-turn winding fault
• machine learning
• Hilbert transform