## 4 IoT Overview for Predictive Maintenance in Smart Manufacturing
In the first stage, they identified the organization's critical assets; then, they obtained data from IoT sensors positioned on the machines, which are kept in cloud or database storage. The raw data are pre-processed in the third stage to generate higher prediction reliability and accuracy. The data are split into training and test data in the next stage. In the fifth stage, the output is analyzed and evaluated based on the prediction accuracy of the ML algorithms. In the final stage, the  
Figure 16: Schematic representation of the features and advantages introduced by the PdM. model is deployed to the company. To create the PdM model, supervised machine learning algorithms were applied. During the operational phase of a plant, all the sensors involved in monitoring record data, and in this regard, [PERSON] et al. examined the combined use of value chain and maintenance data to enhance value chain performance via forecasting [100]. The chosen strategy comprises theoretical and practical experiments that provide a fresh idea for an ANN model with sensor data inputs and a PdM platform. Their work aims to monitor the correct functioning of the wood-splitting saw in a woodworking company. Their solution monitors the blade temperature, blade tensioning wheels temperature, and vibrations because, with the increase in productivity, the wood splitter is subjected to greater stresses than those designed for, and heating would compromise both the saw's mechanical resistance and the product's quality. [PERSON] et al. developed a PdM system for production lines based on real-time data acquired from IoT sensors [101]. The system uses machine learning methods to identify signals of probable failures before they happen. The authors performed a comparative analysis between the XGBoost algorithm and random forest models, demonstrating that combining these two models produces better results than the individual algorithms. Figure 18 shows the architecture proposed by the authors of reference [101]. Figure 17: The architecture proposed by [PERSON] et al. in [99] is divided into three layers. The machines under observation make up the first stratum. The edge device layer, which mainly comprises sensors and collectors, makes up the second layer. The Internet service platform, which consists of a cloud server and software operating on it, defines the third layer. The program occupies the final layer. In order to identify priority areas and enable digital technologies for IoT deployment, predictive maintenance, remote examination, and data-management platforms, [PERSON] et al. in [102] proposed a strategy for IoT implementation in railway asset maintenance by adopting a qualitative methodology through a series of focus group workshops. Note-taking, idea mapping, and audio re-recording were some data-collecting techniques used. Concept maps were incorporated for modeling and concept analysis, while audio recordings were transcribed and utilized for theme analysis. Existing PdM studies typically concentrate on a predictive model without addressing maintenance decisions or optimization based on known system breakdown patterns. To address this issue, [PERSON] et al. in [103] propose a predictive maintenance model for Industry 4.0 (PMMI 4.0), which uses a novel proposed solution PMS4 MMC to support an optimized maintenance planning plan for multiple machine components driven by a data-driven long short-term memory (LSTM) model for RUL (remaining useful life) estimation [104], demonstrating the validity of their proposal through a real case study with related data. Most of the state-of-the-art ML approaches for PdM use various condition monitoring data such as vibration, current, temperature, etc., and failure data to predict the remaining useful life (RUL) of components; what is not monitored is the component wear and tear because it is not easy to identify. [PERSON] et al.