A PFM System design for intelligent manufacturing in Industry 4.0 using FG Technology

In recent times, the use of predictive maintenance (PdMt) methods has been widespread in organizations to manage the well-being of Manufacturing Machinery (MM), thanks to the rise of Industry 4.0 (I4.0) and the integration of intelligent systems and Deep Learning (DL) in Artificial Intelligence (AI). The data created by the Industrial Internet of Things (IIoT) facilitates the accessibility of data and automated execution of processes in Industry 4.0. Digital resources consist of the information generated by the IIoT. The use of industrial big data is to forecast the malfunction in MM. PdMt empowers the organizational leader to make informed decisions on the maintenance or replacement of an accessory when an actual breakdown occurs, preventing any adverse impact on the entire manufacturing stream. Hence, Industry 4.0 necessitates a proficient equipment management system to enhance the allocation of jobs and implement a PdMt model. This work introduces a resource management system for fog computing that combines the Genetic Algorithm (GA) with DL techniques to enable PdMt. The PdMt framework uses Long Short-Term Memory Networks (LSTM) utilizing real-time observations. GA-LSTM for PdMt exhibits the most superior performance, boasting the shortest processing time of 291.32 seconds, the lowest operating expenditures amounting to $385.23, and the lowest energy usage of 10.36 joules [16].