At the present stage, the environmental testing equipment failures occur frequently and it is necessary to automatically judge whether the equipment fails. Although the equipment does not trigger alarm equipment in the equipment room, the monitoring data is beyond the normal working range. In order to solve this kind of equipment fault problem, a method of target detection and recognition based on Faster R-CNN is proposed. By training the convolutional feature map for the manually marked image data, the detection and recognition model of switch, indicator and digital instrument in this scenario is obtained. Experiments show that the Faster R-CNN algorithm can detect the faults of the three kinds of equipment with different shooting angles, occlusion, and different illumination conditions, and can achieve ideal results and basically achieve the efficiency of real-time monitoring.