ASSGCN Model Based Vibration Signal Reconstruction in Motor Vibration Testing

For modern electric powertrain applications (wind, electric vehicles/ships/aircrafts, horizontal ellipsis ), the vibration analysis of the electric motor is one of the most important tasks. Normally, a large number of vibration sensors are placed evenly around the stator of the prototype to sample the acceleration and vibration signals. To decrease the vibration testing cost and time, in this article, an attention-based spatial-spectral graph convolutional network (ASSGCN) model is proposed to reduce the number of sensors to reconstruct the vibration signal of the motor. Three spectral features of the vibration signal are modeled separately, and the correlation of the operating condition force, acceleration and vibro-impedance matrices are investigated and analyzed in the spatial dimension. Via dynamic correlation analysis of spatial configuration and spectral response, the proposed ASSGCN model predicts vibration signals at different sensor sampling points. A 21 kw integrated permanent magnet synchronous motor testing rig with Bruel and Kj AE r's vibration sensing equipment is employed to test the proposed ASSGCN model and the proposed method successfully reconstructs the vibration source signal and achieves well performance.