In this study, a signal-based predictive fault-detection approach is developed to identify potential faults within an electric motor. In order to evaluate the performance of the proposed approach, first artificial motor vibration data are produced and used as a base line for analysis and assessment of the methodology. After successfully confirming proof of concept by detecting all fault frequencies hidden within the artificial data, the approach is then applied to the experimental data to see whether it can be accepted as a suitable potential fault detection tool for healthy electric motors. As the keystone of the study, algebraic summation operation (ASO) is introduced for predictive fault detection. ASO is built and based upon the redundant nature of stationary wavelet transform (SWT). Although similar to the SWT, the down sampling operation defined for the perfect reconstruction of SWT is omitted in ASO to amplify the redundancy within the transform. In other words, the redundancy obtained during decomposition is conserved on purpose with the goal of amplifying potential fault frequencies in electric motor vibration spectra and allowing for more robust and predictive fault detection.