Analysis and Non-Invasive Diagnostics of Bearing Faults in Three-Phase Induction Motors

Abstract

This article focuses on the analysis and non-invasive online diagnostics of the operating condition of bearings integrated into three-phase squirrel cage induction motors, an electric machine that, due to its construction and operational characteristics, has a significant presence in the industry. The proposed signal-processing analysis tool is based on the non-invasive monitoring of stator electrical currents. To improve robustness in the diagnosis of bearing faults beyond the state-of-the-art, a hybrid approach was employed. The Short-Time Fourier Transform (STFT) and Park’s Vector Approach (PVA) were combined and applied to the stator currents. This hybridization allowed the benefits of both methods to be combined: (i) proper evaluation of time-varying phenomena and (ii) the ability to distinguish the type of fault affecting the bearing. To demonstrate the feasibility of the approach, comparisons were made between the proposed hybrid technique and both the STFT and the Extended Park’s Vector Approach (EPVA), which have been previously considered in the diagnosis of these and other induction motor faults. The validation of the proposed solution was conducted through computational simulations and laboratory tests, ultimately aiming to generate a database of results to inform future research in this area. To emulate bearing failures in an experimental context, artificial damage to bearing components was introduced.

Keywords

• induction motors
• bearing faults
• condition monitoring
• stator current spectrum
• Extended Park’s Vector Approach (EPVA)
• Short-Time Fourier Transform (STFT)