A Process Of Developing An Internet Of Things Based Model For Manufacture Monitoring In Automobile Industry

Abstract

In this modern era, monitoring various aspects like temperature, pressure, equipment status, working environment, inventory, and productivity is complex. The deviation in the values due to inaccurate measurement will have a significant effect on productivity. To overcome this problem, the Internet of Things-based sensor system plays an essential role in effectively monitoring the manufacturing systems. In this research, a real-time monitoring system using Internet of things based sensors for big-data processing and prediction has been proposed for applications in the automobile industries. Initially, the IoT-based sensor collects important parametric data. The obtained big-data is processed using Apache Kafka, which is a type of message quence. For further real-time processing, and Apache storm engine is proposed. MongoDB is used to store the information from the sensor data collected during the manufacturing process. DBSCAN [Density-Based Spatial Clustering of Applications] and Random forest-based noise-based outlier detection are used to remove the outlier information from the sensor data. It points out the fault detection during the manufacturing process. The model's outcome proves that the Internet of Things-based sensors for processing big data seems to be a successful process during product manufacturing. From the outcome, it was found that the proposed model is having 100% more accuracy than the other existing model. The study's outcome proves that it may help the automobile management regarding unexpected loss due to fault in the initial stage of manufacturing, and it also improves the decision-making for the manufacturing process.