The working principle and hardware composition of a novel absolute positioning device for high-speed maglev trains are introduced, and the possible fault types of each circuit module are analyzed in detail. A corresponding SVM-based fault detection method is designed to locate and classify faults in this new type of sensor. By constructing a simulated dataset of 100 sets of fault and normal states at various nodes, frequency-domain and time-domain features are extracted from each set as SVM inputs to verify the reliability of the proposed fault detection method. Simulation results show that the designed fault diagnosis method achieves a high fault recognition rate and can meet the practical application requirements of the fault detection system for absolute positioning sensors in high-speed maglev trains.