Advanced Residual Optimal Mapping Approach for Precise Detection of Stator Faults in Induction Motors

Abstract

Induction motors, known for their reliability and efficiency, are widely used in various industrial applications. However, their susceptibility to failure, particularly in harsh environments, poses significant operational challenges. Early detection of faults is essential to avoid unplanned downtime and costly repairs. In this study, we propose a novel diagnostic approach derived from the analysis of four distinct types of harmonics: (i) Rotor Slot Harmonics (RSH), (ii) time harmonics (TH), (iii) eccentricity fault harmonics (EFH), and (iv) rotor bar fault harmonics (RBFH). These harmonics are examined through fast Fourier transform (FFT) analysis, enabling the identification and characterization of various fault types. Experimental results indicate that TH appears to be the most sensitive harmonic for detecting stator faults, with a residual fault detection value of 4.5 A at full load and 5.05 A at half load. Real-time FFT processing of stator current signals, compared to a healthy reference signal, gives rise to the advanced residual optimal mapping (AROMA) approach. This approach allows for accurate detection and severity assessment of stator faults, where fault severity values are measured at 33.65 A for full load and 17.24 A for half load. Our innovative strategy seeks to utilize the remaining differences among the harmonics displayed by the established healthy state stored in the database and those detected in the real operational healthy state. This enhanced sensitivity and precision in fault detection and severity assessment aim to significantly reduce unplanned downtime and associated costs.

Keywords

• Artificial intelligence
• diagnosis
• fast Fourier transform
• harmonics
• induction machine
• inter-turn short circuit