Advanced Artificial Intelligence Drivers and IoT Network Systems for Data Collection and Network Transmission in the Industry 5.0 Era

The primary bases of Industry 5.0, the Internet of Things (IoT), artificial intelligence (AI), and big data (BD) may serve as a basis for applying the concepts of smart manufacturing, innovative goods and services, and predictive maintenance (PDM) into practice. Assessing the remaining useful life of components using failure data and state monitoring information constitutes the AI techniques for predictive maintenance (PDM), which are critical in Industry 5.0. However, it is sometimes challenging to identify the annotation of component usage, creating an open problem for obtaining accurately labeled information and understanding it. This problem needs to be rectified if Industry 5.0 progress is proceeded with. In order to solve the PDM job, this study presents a new data-driven decision system (DDS) based on AI that overcomes the earlier issue. It does by concentrating on an actual industrial use scenario that involves modern processing and monitoring machines. Specifically, the most fundamental components of the recommended DDS include cloud-based storage, data interpretation, predictive modeling, recognition of features using Principal Component Analysis (PCA), and data collecting. An efficient prediction model called the unique moth flame search-tuned light gradient boosting machine (MFS-LGBM) technique is presented in this work. By enabling the ongoing gathering of identified samples and offering a data analysis environment to assist the maintainer/operator, the suggested approach resolves the PDM task. The research findings showed that when compared to the previous approaches, the suggested approach accomplishes the greatest conceivable balance between comprehension, computational effort, and forecasting accuracy in terms of quality estimation. Based on the suggested methodology, the performance is examined in terms of RMSE (600.10), MAE (390.60), [Formula: see text] (0.790) and MAPE (25.70) measures. By incorporating it into the cloud structure, the suggested technique minimizes maintenance expenses and enhances production efficiency by improving machining processes, assisting maintainers, and delivering current operating risk alerts.