Application of Multi-Measurement Vector Based on the Wireless Sensor Network in Mechanical Fault Diagnosis

Abstract

In order to solve the problem of low positioning accuracy of mechanical fault diagnosis, a polarization GPR imaging reconstruction algorithm based on the MMV model was proposed. The algorithm was mainly based on the joint processing of the measured data of multiple polarization channels to achieve the reconstruction of the reflectance of the detection scene corresponding to each polarization channel. The simulation data processing results based on FDTD showed that compared with the traditional SMV model polarization imaging algorithm, the proposed imaging algorithm could improve the accuracy of target location reconstruction and the ability of background clutter suppression significantly. Compared with the SMV model, TCR obtained by the MMV model increased by 30%. As for the imaging results at different noise ratios, TCR obtained by the MMV model was 10% higher than that obtained by the SMV model. And when the ratio of available real data samples decreased to 25%, the sample data generation based on the adversarial generation network could greatly improve the classification accuracy of the fault diagnosis model. It could realize the detection of the target better, so as to locate faults accurately.