In this paper, we propose an approach for fault diagnosis of railway train based on a combination of fractal theory and k-mean clustering technique. First, the fractal dimensions of the waveforms were calculated to analysis the singular characteristics of the waveforms. Second, the k-mean clustering was used to cluster the singular characteristics in order to determine the running state and fault diagnosis. It can effectively monitor the running state and safety performance of the railway train, and provides technical support for the safe operation and maintenance of the railway train. The method has the advantages of strong real-time and high accuracy for fault classification, which has a certain reference value for analysis of uncertainty and irregular waveform.