AWS IoT Core and Amazon DeepAR based predictive real-time monitoring framework for industrial induction heating systems.

For embracing Industry 4.0 or the fourth industrial revolution, various industries are implementing process automation and have started leveraging interconnectedness through Industrial Internet of Things. With the help of cloud based solutions, data captured through distributed IoT (Internet of Things) sensors are readily available for system monitoring enabling effective and smart monitoring with higher predictive results. The industries that leverage induction heating systems are also aiming at accomplishing digital transformation to adopt Industry 4.0 and guidelines in this regard are most sought after at this moment. Establishing a real-time monitoring framework for the induction heating systems is a critical enabler for Industry 4.0 adoption. The authors have presented a case study of the real-time monitoring framework for industrial induction heating systems that will enable digital transformation of such industries. This framework is based on AWS (Amazon Web Services) IoT Core and Amazon DeepAR. It takes the full advantage of Amazon cloud features and functionalities. The case study includes development of a web application using React, Node.js and Express to display various dashboards and predictions as well. It would send notifications whenever required based on the configurations set. The dashboards generated show the furnace temperature and power levels with an indication whether they are above the desired levels of temperature and power as well as corresponding performance metrics. The framework uses DeepAR algorithm from Amazon SageMaker to generate forecasts so that preventive measures can be initiated in advance. The same framework can be extended to other industrial processes as well. [ABSTRACT FROM AUTHOR]