Machine Learning-driven Quality Control Strategies for Industrial IoT Applications

This paper introduces a new solution for improving Quality of Control in Industrial Internet of Things (IIoT) systems by using a machine learning approach. The planned system utilizes monitoring data originating from different sensors installed in the production line to detect faults as well as estimate maintenance requirements. Besides, the proposed system uses the state-of-art machine learning techniques like Convolutional Neural Networks for defect detection and Recurrent Neural Networks for Predictive maintenance with high accuracy and efficiency. Computer simulations indicate a high increase in the coverage of defects, predictive capability, and overall product quality. The combination of IoT and machine learning presents a convenient and evolutionary solution that becomes progressively enhanced as more data becomes available, and give a solid framework for the sustained optimization of standards in industrial practices. Future work seeks to enhance the developed models and extend the implemented system’s functionalities to other application domains and bigger data sets.