Mixed Eccentricity Fault Detection of Induction Motors Based on Variational Mode Decomposition of Current Signal

Mixed eccentricity faults in squirrel cage induction motors (SCIMs) are challenging to diagnose due to their subtle influence on the stator-current signal. Several research gaps remain in this field, including the limited investigation of fault severity levels and the scarcity of studies addressing fault detection under full-load conditions. Motivated by these gaps, this study proposes a diagnostic approach based on the variational mode decomposition (VMD) of the stator current. This paper proposes a diagnostic approach based on VMD of the stator current. The current signal is decomposed into intrinsic mode components, which are further separated into approximated and detailed signals. By focusing on the detailed signals and removing the fundamental frequency, the proposed algorithm highlights the spectral components associated with the mixed eccentricity. Experimental validation was carried out on a 1.5 kW SCIM connected directly to the power grid and tested under three loading levels (12.5%, 50%, and 100% of the rated load). In all nine experimental scenarios, the method successfully distinguished the healthy motor from faulty conditions with 20% and 30% mixed eccentricity severities. These results demonstrate that the proposed VMD-based method provides a reliable and quantitative tool for rotor fault diagnosis under varying load conditions.