Permanent magnet synchronous motors (PMSMs) are widely utilized across various industrial sectors due to their remarkable efficiency, accuracy, and dynamic performance. These industries include robotics, electric vehicles, and solar energy systems. The ability of PMSMs to integrate all three of these characteristics preliminary explains their widespread usage. Although PMS systems offer many advantages, they are prone to faults that could compromise their dependability of operations or performance. Within the framework of this thesis, MATLAB/Simulink is used to build a consistent simulation model in order to examine the behavior of PMSMs in both healthy and imperfect states. Among the several fault conditions the model considers during its analysis are open-circuit faults, isolated turn faults, ground faults, and demagnetization faults. Key motor metrics like torque, RPM, rotational angle, and winding currents are generated in a comprehensive dataset. This is achieved by repeating both functional and non-functional environments all through the research. Using this dataset will help to build the basis for faults diagnosis and the development of diagnostic algorithms. The main goal is to gather essential data that may be used to train machine learning models with the aim of early fault discovery in PMSMs, not investigate the defects themselves. Thanks to the simulation environment, one is able to analyze numerous input parameters like voltage and load torque and their effects on motor performance in the presence of faults. This work contributes to the development of more dependable PMMS systems by means of the facilitation of effective monitoring and maintenance activities based on simulated fault scenarios.