Effective Monitoring and Functioning of Industrial Machinery with Various Control Parameters Using IoT

A new paradigm for improving industrial equipment control has developed with the introduction of the Internet of Things (IoT) with machine learning and cuttingedge optimization techniques like simulated annealing. The abstract of this study discusses how the Internet of Things (IoT) and simulated annealing optimization techniques can be used to effectively monitor and operate industrial machinery. To enable real-time data collection from numerous sensors and devices placed in industrial contexts, the suggested approach makes use of IoT technology. These sensors offer useful data on machine characteristics like temperature, pressure, vibration, and power usage. A central control system receives the collected data and uses it to analyze and make decisions. Simulated annealing, a metaheuristic optimization algorithm, is used concurrently to optimize the industrial machinery's control settings. IoT and simulated annealing work well together since they have several advantages. First, real-time monitoring made possible by IoT enables preventive maintenance and prompt discovery of anomalies or potential problems. Critical metrics can be monitored to spot deviations from normal operation and take remedial action to stop breakdowns or performance decline. Second, by optimizing control parameters during simulated annealing, energy efficiency, wear and tear, and productivity are all increased. In conclusion, a strong framework for efficient monitoring and operation of industrial machinery is provided by the integration of IoT and simulated annealing optimization approaches.