IoT based Real-Time Monitoring System for Industrial Automation with Big Data Analytics using Machine Learning Approach
Since the last decade, voluminous volumes have been created as Internet of Things (IoT) devices have been more miniaturized. However, without analytical power such data are not meaningful. Many huge data solutions, IoT and analytics have enabled individuals to gain significant information on enormous data created by IoT devices. These solutions, however, are still in its infancy and a full survey is lacking in the area. A solution for efficient monitoring of the production process may be seen in the current techniques such as the IoT (internet of things)-based sensors. This paper proposes a real-time monitoring system which uses IoT-based sensors, large-scale data processing and the paradigm of machine learning. This article mixes the concept of industrial workstation Raspberry Pi with IoT industrial automation. The programming is in python language and uses the Raspberry Pi as controller and server. Apache Kafka, Apache Storm, and the MongoDB are used to store sensor data from the production procedure in real-time as a messaging queue on the suggested big data processing platform. All sensor data is gathered via raspberry pi. All data is used by the Internet of thing platform on a distant basis. This gives an industrial example of the blade ageing system of the cutter-tool and monitors current on the site using raspberry pi as server. The solution presented should support management through better decision making and assist reduce unexpected losses caused by defects during production processes.