Space Vector Theory, also known as Park's Vector Method, is a frequently utilized technique for analyzing, modeling, and controlling electrical machines and drives. In motor diagnostics applications based on Park's Vector trajectory patterns, the assistance of domain experts is indispensable for Fault Detection and Diagnosis (FDD). Yet, simultaneously occurring faults may lead to feature overlap or accumulation, posing additional challenges for identification. In this paper a comparative analysis of automating Park's Vector Approach is presented, with combining the method with advanced ML techniques. Utilizing a magnetics-based Permanent Magnet Synchronous Motor (PMSM) model, a custom simulated fault dataset is created for training purposes. The dataset is used to train three distinct models: a Siamese Network, EfficientNet and the Vision Transformer model. Throughout the evaluation, key classification metrics including the F1-score, precision, and recall are analyzed, alongside an examination of the training curves. The results indicate that the Park's Vector Approach combined with State-of-the-Art ML models efficiently detects anomalies and multiple faults. It highlights the models' ability to identify local features, accurately classify, and detect simultaneous faults, which is often challenging for domain experts due to the difficulty in distinguishing fault features of multiple faults even through visual inspection.