<p>Bearings are vital parts of electrical motors and centrifugal pumps. This study presents a comprehensive analysis of an induction motor coupled with a centrifugal pump as a case study, primarily focusing on detecting bearing failures. Time-domain vibration signals and their corresponding frequency spectra were experimentally acquired and analyzed to extract diagnostic features. Based on experimental vibration data and mechanical symptoms, the results revealed that, under normal operating conditions, the bearing cage and rolling elements were not rotating properly. Signal processing techniques, including amplitude analysis and spectral methods, successfully identified significant anomalies in the motor’s vibration behavior and ball pass frequency outer ring (PBFO). These anomalies were attributed to the malfunction of the bearing components, particularly the non-rotation of the cage and balls. An advanced signal processing method based on experimental data was extracted to show the difference between healthy and failed bearing conditions. Specifically, power spectral density (PSD), wavelet energy, and wavelet-based time-frequency representations were computed in MATLAB to evaluate the bearing condition based on experimental time-domain data.</p>