RETRACTED ARTICLE: Internet of Things Based Industrial Automation Using Brushless DC Motor Application with Resilient Directed Neural Network Control FED Virtual Z-Source Multilevel Inverter Topology

Internet of Things (IoT) is a high-speed communication technology which has carnal substances or devices entrenched with sensors, system connectivity, which allows to receive and interchange data. Industrial Monitoring and Control is required to assemble all the material information, statistics, and data related to the various industrial processes, motors, machines and devices employed in industrial premises. The technological improvements, remote control and monitoring via communication techniques such as wireless sensor network techniques have been widely used in Industries. Competitive advantages over AC motors make for DC motors to replace other electrical engines in applications stretching from high-speed automation to electric motorbikes. BLDC drives are very popular in many industries, at present automation are added standard, Virtual Z-source multilevel is a respectable optimal that can boost the output voltage of the drive. A novel soft computing based Resilient Directed Neural network (RDNN) found Virtual Z-source multilevel inverter, for BLDC motor drive control to make the system balanced when the load is unbalanced and to reduce the electrical torque pulsation. In this work, the utilization of the RDNN to tackle the reduced harmonics issue in VZS-MLI converters is proposed. This strategy permits active voltage control of the crucial and besides concealment of a particular set of harmonics. The performance is evaluated in various emphasis levels of the different control models. The sensors monitor the technical motor parameters like greatest rise and fall time, topmost overextend and inaccuracy value of load current and voltage in BLDC machine. Then the measured values are sent to the processing unit, which will analyze and display the parameters, where the processing unit also communicates with Gateway module to send information to cloud database for remote monitoring. The system also presents the Automatic and manual control methods to stop or start the BLDC machine to avoid system failures.