# Real-Time Maintenance Optimization with Industrial Internet of Things
## 5 Discussion  
This study presents a novel real-time maintenance optimization approach that integrates Industrial Internet of Things (IIoT) technologies with real-time, adaptive optimization. Unlike previous studies primarily focusing on anomaly detection or early fault identification, our method goes a step further by actively optimizing maintenance actions in real time based on continuous feedback from the system. One major distinction lies in the depth of real-time optimization: while existing studies typically stop at detection, our model supports continuous decision-making to improve performance and reduce costs. For instance, [PERSON] et al. (2025) emphasize anomaly detection in consumer IoT devices using TinyML and deep learning, but do not address dynamic optimization [(20)]. In contrast, we propose a mathematical model that uses IIoT data to adaptively minimize maintenance costs while maximizing system efficiency. For example, consider a CNC milling machine used in automotive component manufacturing, equipped with several IIoT sensors to monitor its performance. The IIoT system collects various data types, including vibration data from accelerometers placed on the spindle and bearing housings, with a sampling interval of every 10 s for continuous monitoring. Temperature data are gathered from sensors on the spindle and motor, averaged every minute. Additionally, current consumption is measured on the main motor and tool drive, with readings taken every 5 s. Finally, operational time and cycle count are tracked by the machine controller and recorded per production cycle. The mathematical model utilizes these data to predict the likelihood of failure for critical components such as the spindle or bearings using machine learning techniques like decision trees or logistic regression. By integrating these predictions, the model helps optimize the timing of maintenance tasks. It balances the cost of preventive maintenance with the cost of failure, ensuring maintenance is only performed when necessary. The model works by minimizing a cost function that incorporates both maintenance and failure \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Conventional**} & \multicolumn{2}{c}{**Real-Time**} \\ \hline \multicolumn{5}{c}{**Length of Maintenance Periods**} & \multicolumn{4}{c}{**Reliability**} \\ \hline  & **2** & **3** & **4** & **5** & **0.6** & **0.95** \\ \hline PT20 1 & 60 & 60 & 60 & 60 & 74.25 & 61.975 \\ TI20 2 & 19,245.77 & 12,781.93 & 6511.53 & 2982.61 & 30,983 & 38,987 \\ TMC20 3 & 6430.64 & 2976.32 & 1723.05 & 1127.63 & 7529.5 & 8744.7 \\ TP20 4 & 12,815.13 & 9805.61 & 4788.48 & 1854.98 & 23,454.5 & 30,242.3 \\ HP 5 & 213.59 & 163.43 & 79.81 & 30.92 & 315.88 & 487.97 \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 4: Comparison of the main parameters of conventional and IIoT-supported real-time optimization-based maintenance strategies. costs. As a result, maintenance is no longer scheduled at fixed intervals. Instead, the model triggers maintenance only when the data signals an increased risk of failure. [PERSON] et al. (2024) apply IoT and the Analytic Hierarchy Process (AHP) to prioritize parameters in diesel generator systems, but lack a dynamic, real-time optimization layer ([PERSON], 2021). [PERSON] and [PERSON] (2021) use machine learning models for early failure detection ([PERSON] and [PERSON], 2023), and [PERSON] et al. (2023) offer a literature review on sensor-enabled composites ([PERSON] and [PERSON], 2023), but both primarily focus on monitoring and detection rather than integrated optimization. The existing studies are largely data-driven, applying machine learning or statistical techniques, whereas our approach combines data with a model-driven framework for more strategic and intelligent decision-making. This allows us not only to predict failures but also to plan maintenance activities in real time with clear economic and operational objectives.