Underwater thrusters, vital components in various underwater vehicles, have been extensively studied for fault identification and classification. However, the automatic and unsupervised assessment of fault levels remains largely unexplored. This article presents a novel approach integrating physical information into a data-driven architecture and training process, enabling automated and unsupervised fault identification and evaluation. The process begins with constructing a physical model of the thruster and estimating its low-fidelity current based on the vehicle's velocity and the thruster's rotational speed. An improved SimGAN is then utilized, in combination with the vehicle's motion state, to map this low-fidelity current to its high-fidelity counterpart in the physical space. Statistical features are extracted from the absolute errors between the high-fidelity current and the measured current as conditions for the discriminator. The framework achieves thruster malfunction identification and assessment by analyzing the discriminator's output. Ocean trial data validate the effectiveness of this framework, with experimental results showing its satisfactory performance in fault identification, level evaluation, computational complexity, and robustness when compared with advanced methods.