The wireless sensing by multi-sensors and online monitoring technology for rail vehicle traction motor bearing is one of the key technologies to ensure the reliable operation. However, existing methods suffer from the problems of large data volume transmission difficulties and visualization of small characteristic data is not obvious. Therefore, sparse online monitoring method for rail vehicle traction motor bearing fault characteristics is proposed in this paper. The minimum entropy deconvolution method of particle swarm optimization multi-point optimal adjustment ( PSO-MOMED) is used to extract the fault characteristic signal of motor bearing under background noise. The method of discrete cosine transform (DCT) compression sensing is used to acquire the bearing characteristics with a small number of multi-sensors. Meanwhile, sparse visualization of bearing fault features is enhanced based on high order frequency weighted energy operator (HFWEO). Furthermore, the effectiveness of the proposed method is verified by setting up a test bench and measuring a line on site. The experimental results show that when the signal-to-noise ratio is -10 dB, PSO-MOMED method can extract fault characteristic signals more effectively than the traditional method. In the case of 90% compression, the frequency components of bearing fault features can be clearly characterized from the sparse perception signals of traction motor bearing fault features. It effectively