Currently, in the industrial sector, electric motors constitute 40–50% of the total equipment serving the operation and production process, and also account for a corresponding proportion of electricity consumption. Therefore, diagnosing motor faults becomes crucial, helping to predict incidents, reduce maintenance costs, and prevent unforeseen production losses. In addition to traditional methods, the motor current signature analysis (MCSA) method is widely preferred thanks to non-invasive technique. This method allows for direct diagnosis, without needing to halt operations or directly intervene with the motor. It utilizes current transformers already present in the motor drive system, does not need additional sensors, and low implementing cost. This method can be performed online, allowing for easy evaluation of motor health remotely. Park’s vector approach (PVA) and fast Fourier transform (FFT) are two important techniques used in the MCSA method to detect motor faults. In this paper, these two techniques have been exploited to evaluate and diagnose motor faults based on simulated and real data. Experimental results demonstrate the MCSA method's potential for diagnosing motor faults and highlight the challenges associated with its practical application and deployment.