Recently, inter est in functional safety has surged because vehicle technology incr easingly relies on electr onics and automation. Failure of certain system components can endanger driver safety and is costly to address. The detection of abnormal data is crucial for enhancing the reliability , safety , and efficiency . This study introduces a novel anomaly-detection method of designable generative adversarial network anomaly detection (DGANomaly). DGANomaly combines the data augmentation method of a designable generative ad versarial netw ork (DGAN) with a generati v e adv ersarial network anomal y-detection data classification technique . DGANomaly not only generates virtual data that are challenging to obtain or simulate but also produces a range of statistical design v aria b les for nor- mal and abnormal data. This approach enables the specific identification of normal and abnormal design variables. To demonstrate its effecti v eness, the DGANomal y method was applied to an electric power steering (EPS) model when multiple degradations of gear stiffness, gear friction, and rack displacement were considered. An EPS model w as constructed and validated using simulation programs such as Prescan, Amesim, and Simulink. Consequently, DGANomal y exhibited a higher classification accuracy than the other methods, allowing for more accurate detection of abnormal data. Additionally, a clearer range of statistical designs can be obtained for normal data. These results indicate that the statistical design variables that are less likely to fail can be obtained using minimal data.