In order to ensure the safe operation of electromagnetic suspension (EMS) maglev trains, it is necessary to pay attention to the control loop performance of the suspension system. The suspension system with closed-loop control is tuned to achieve excellent performance at its early stage of operation. After running for a period of time, the control loop may encounter problems e.g., degraded operation, and paralysis may occur in severe cases. In order to quantify the control performance of the suspension system in an explicable manner, this paper proposed a data-driven control loop performance evaluation method based on fractal analysis, which does not require any external sensors and can be applied without data source restrictions such as dimension, volume and resolution. The control loop performances of such suspension systems were monitored, analysed, and evaluated by cross-sectional study, based on the field data of a commercial operation line in the commissioning stage. Furthermore, the track condition was revealed by capturing performance changes of the suspension system running on different guideway girders. The results demonstrate that the proposed method enables early warning of the degeneration of the suspension systems and the track.