The failure diagnosis of railway vehicle door system is carried out using a test bench and machine learning software for the fast and accurate classification. The signal length deviation exists in actual collected data of normal operation and abnormal failures with a time delay. The traditional data multi-segmentation technique for feature extraction has shortcomings by assuming that the measured time-based signals have the same operating time. However, the uniform data segmentation has a difficulty due to the deviation of measured data length. A method of converting time-based data into position-based data was performed to overcome the deviation problem. A method of optimized single-zone data using a genetic algorithm was proposed to improve the classification performance and to reduce computation time, instead of existing the multi-segmentation technique. A principal component analysis-based feature dimensional reduction with explained variance ratio was used to reduce the effect from multi-collinearity of features. Finally, the combination of the proposed methods was compared with individual methods to validate the classification performance by using support vector machine and other classifiers. It was confirmed that the proposed combination method shows the highest classification accuracy of 99.84%. INDEX TERMS failure diagnosis, genetic algorithm, principal component analysis, railway vehicle doors