The turnout switch machine, a vital outdoor component of railway signaling, controls train steering amidst complex operations and high frequencies. Its malfunction significantly disrupts train operations, potentially causing derailments. This paper proposes a sound-based fault diagnosis method, termed ERS (a method combining EMD, ReliefF, and SVM), for effective monitoring and detection of turnout switch machines. The method employs Eigenmode Decomposition (EMD) to smooth the sound signal, reduce noise, and extract key statistical parameters of both the time and frequency domains. To address redundant information in high-dimensional features, the ReliefF algorithm is utilized for feature selection, dimension reduction, and fault classification based on weighted parameters. Subsequently, the selected feature parameters are used to train the Support Vector Machine (SVM). A comparison with results obtained without ReliefF feature selection demonstrates the necessity of this step. The results show that the fault diagnosis accuracy reaches 98% in the positioning work mode and 95.67% in the reversing work mode, verifying the method’s effectiveness and feasibility.