Systematic Analysis of Machine Learning Techniques for Machine Idle Time Detection Using Industrial Internet of Things (IIOT)

Given the IOT's huge success in linking consumer electronics, we see a natural trend toward connecting devices in industrial settings, dubbed the Industrial Internet of Things or Industry 4.0. Conventional production systems often include several machines and buffers, each of which may have an impact on the state of the machines surrounding it. Machines may remain idle if upstream components do not arrive fast enough, or if demand for those components exceeds capacity in the downstream sector of the production chain. Idle equipment may waste energy without adding to output if suitable energy management measures are not followed. Modern machine learning (ML) approaches are beneficial in increasing idle time detection. This manuscript offers a thorough analysis of the current academic literature about the integration of idle time detection within the industrial Internet of Things (IoT).