The development of the instrument sharing platform has increased the utilization rate of instruments and equipment in various universities. However, during the use of the equipment, the fault detection of the equipment has not been improved. In view of the above problems, this study collected relevant data of medical imaging equipment, adopted the two-way feature selection method of PSO_RF for feature selection, then built a fault detection model based on LightGBM (Light Gradient Boosting Machine), and applied it to the fault detection of medical imaging equipment. Through the establishment of the standard evaluation system and the comparison of fault diagnosis results by different models, compared with the traditional machine learning algorithm, this model has a better performance in the accuracy rate, recall rate, F1 value and other evaluation indicators of fault detection, which has a positive role in accelerating the discovery of instrument fault points and improving the utilization rate of instruments.