## 4 IoT Overview for Predictive Maintenance in Smart Manufacturing  
Companies are changing drastically in the smart manufacturing age as a result of the integration of IoT and digital technology. Before the advent of Industry 4.0, safety was the main priority, with the goal of shielding machinery and personnel from dangers and mishaps. However, attention has steadily turned to PM techniques, which maximize operational effectiveness while guaranteeing safety. Predictive maintenance relies on real-time data analysis, allowing companies to predict failures and malfunctions rather than react to them. This approach improves the reliability and efficiency of production processes and reduces operating costs and downtime, significantly increasing productivity. These benefits are briefly highlighted in Figure 16. In particular, many companies have adopted PdM techniques [96]. This approach improves resource management and scheduling PdM interventions, preventing unplanned and expensive machine downtime. Intelligent manufacturing and maintenance are now the focus of Industry 4.0 due to the rapid growth of human civilization from the information age to the smart automation era. The pressing need to detect upcoming breakdowns and reduce unplanned downtime of industrial equipment might be met by data-driven intelligent predictive maintenance (IPdM); it is considered a vital component of the IoT in the future [97]. \begin{table}
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Table 3: _Cont._Technologies used for predictive maintenance include sensors to monitor the operating status of components, data analysis algorithms to identify trends and anomalies in tracking data, and decision support systems to plan maintenance interventions efficiently. Predictive maintenance is widely used in high-criticality industries such as manufacturing, aviation, and energy, where system safety and reliability are paramount. This approach reduces maintenance costs, improves system availability, and extends their useful life. Companies adopting smart manufacturing techniques have a competitive advantage as they can bring higher profit margins, reduced maintenance costs, power savings, and high-quality products [98]. In popular PdM procedures, a standard maintenance approach is often implemented for the same defect without considering the machine's life cycle. When the same problem occurs at different life cycle stages, using the same maintenance method will inevitably lead to over- or under-mainance. Furthermore, most PdM studies analyze maintenance choices or predict failures [7]. To provide predictive maintenance of a plant, [PERSON] et al. in [99] adopted an IoT architecture by using smart sensors to monitor the operating status of centrifugal pumps in real time to predict possible failures and intervene before a component failure occurs, thus increasing the level of intelligence of the machine. Figure 17 shows the architecture proposed in Ref. [99]. They started by looking into the typical centrifugal pump failure and the features of the vibration signals that arise after a failure. The authors' system comprises cloud servers, data collectors, and wireless and wired sensors. The wireless sensors are linked to a collector with sophisticated data communication features and are outfitted with 4G, WiFi, RJ45, RS485, and other data interfaces. The ARM (Advanced RISC Machine) CPU and LINUX operating systems are built within the collector processor. The test results showed that the sensors could recognize the signals produced by the centrifugal pump vibrations to automatically identify equipment faults. [PERSON] et al. deduced a PdM approach with IoT sensors to help manufacturing industries predict machine failures before asset failure occurs [98]. Their work discusses a case study from the manufacturing industry where the solution is developed using ML's binary classification support vector machine (SVM), classification and regression trees (C&RT), and boosted classification trees (BCT).