The Effect of Stator and Rotor Faults on the Dual-star Induction Motor Behavior

Abstract

Dual-star induction machines (DSIMs) are widely used in automated production systems that require uninterrupted service. As they mostly operate under dynamic real-time conditions, faults can significantly accelerate the degradation of critical components in variable-speed and load regimes. Therefore, robust monitoring algorithms are essential to assess damage levels and failure modes as faults evolve. For this purpose, a proposed approach leverages the Hilbert envelope spectrum to extract fault-related frequency components from stator current signals, providing a basis for identifying both broken rotor bar (BRB) and inter-turn short circuit (ITSC) faults. A key feature of this methodology is the use of a health indicator, derived from the current envelope spectrum to address challenges associated with damage level and load conditions from a signal processing perspective. Crucially, the relationship between the health indicator, fault severity, and load variations is statistically modeled using surface fitting. The stator current signals required for evaluating the approach are gathered from different simulations of the DSIM, subjected to various levels of damage and load conditions. The results obtained suggest that the third-order polynomial could be sufficient to model the relationship. These comprehensive analyses conclusively demonstrate the efficacy and practical applicability of the proposed fault detection approach, thus contributing significantly to the understanding of electrical machine reliability and fault mitigation.