This article presents a method based on vibration signals to diagnose rotor mixed eccentricity faults in three-phase squirrel cage induction motors (SCIMs). Rotor eccentricity is a typical fault in SCIMs that significantly affects the performance of electrical machines and results in unwanted vibrations. The time synchronous averaging (TSA) signal, which consists of the harmonics components generated by the fault, is obtained via the characteristic frequency of the eccentricity fault. TSA is decomposed into two signals: TSA-regular and TSA-difference, which are respectively similar to approximate and details of the wavelet transform. Based on the fault index, which is the maximum FFT value of the TSA-difference signal, the healthy and faulty motors can be distinguished. The proposed approach is evaluated on an experimental test rig equipped with a 1.5 kW SCIM supplied directly from the power grid under various load conditions. The results illustrate the effectiveness of the proposed approach in detecting the rotor mixed eccentricity faults.

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.