In the last few years, Motor Current Signature Analysis (MCSA) has proven to be an effective method for electrical machines condition monitoring. Indeed, many mechanical and electrical faults manifest as side-band spectral components generated around the fundamental frequency component of the motor current. These components are called interharmonics and they are a major focus of fault detection using MCSA. However, the main drawback of this approach is that the interference of other more prevalent components can obstruct the effect of interharmonics in the spectrum and may therefore impede fault detection accuracy. Thus, we propose in this paper an alternative approach that decomposes the different current components based on the Vandermonde model and implements the tracking of each distinct component in time and spectral domains. This is achieved by estimating their respective relevant parameters using the Gradient Descent algorithm. The results of this work prove to be promising and establish the parametric tracking of the electrical current components using the Gradient Descent algorithm as a reliable monitoring approach.