Acoustic Signal Reconstruction And Signal Source Estimation Of Defect Wheel-bearing Based On Sensors Array Detection

Wheel-bearing is one of the most important machinery parts. Due to overloading, wheel-bearings are easily damaged. Now researchers pay more and more attention to the research on using sensors to detect acoustic emission signal to diagnosis bearing defect. The paper focus on establishing wheel-bearing defect acoustic signal reconstruction methods, and studying moving defect localization of wheel-bearing. The main contents are followsIn order to solve the problem of signal that is incomplete or overlap between multi-sensors, a defect acoustic signal reconstruction method combined with signal energy distribution is established. The signal energy is used to verify the correctness of the reconstruction signal. With established experiment device, defect cycle acoustic signal reconstruction experiments are carried out with three acoustic sensors. It proves that the proposed method can effectively deal with the problem of signal overlapping and obtain complete defect signal.The method of moving defect localization and identification of bearing is another focus in this paper. Defect sources rotate with time continuously when the train moves in a line. Array signal processing methods can track linear moving source. But there is not an effective solution for the nonlinear moving source. To solve the problem of moving defect localization for wheel-bearings, a novel algorithm based on particle filter and multiple signal classification (MUSIC) is proposed in this paper. It introduces two-dimensional circular sensor array to measure acoustic signals of defective bearings. By through of MUSIC, the direction-of-arrivals (DOAs) of defective signal are firstly estimated. After the motion trajectory was calculated by particle filter and DOAs, the defect was located by reference sound source.