Research and Application of Intelligent Inspection of Production Equipment Based on New Generation Information Technology

In the coal chemical industry, an intelligent inspection is essential for improving operational efficiency and safety. However, the existing studies didn’t perform EUI-based load scheduling which may lead to widespread blackouts in intelligent automation systems. Therefore, robot faults and EUI assessment-based load scheduling in intelligent automation systems using RWLA-RNN and HTPP-FLA are proposed. Initially, the robot sensor data are gathered and then subjected to a robot fault prediction system. Here, the robot fault dataset is taken and then pre-processed. Afterward, feature extraction and Za-SHO-based feature selection are done. Next, the robot fault is predicted by utilizing RWLA-RNN. If the robot is normal, then the time synchronization error verification is done in the conveyor/production cycle by using HTPP-FLS. If an error occurs, then the coordination is controlled by EtherCAT; otherwise, the response time of normal robots is minimized by employing Za-SHO. Next, the normal robot data are secured by using FSCC. Then, by using HTPP-FLS, EUI estimation-based load scheduling is done for the operation of the normal robot. On the contrary, if the robot has a fault, then the response time minimization and data security are performed. Next, a command signal is given to stop the operation of fault robots. The results proved that the proposed model achieved a high accuracy of 99.12%, thus outperforming prevailing techniques.