# Real-Time Maintenance Optimization with Industrial Internet of Things
## 5 Discussion
Additionally, while prior work tends to focus on tactical-level challenges such as short-term fault prevention, our study considers maintenance as a strategic tool for improving production efficiency and long-term performance. This makes our solution particularly well suited for smart factory environments where maintenance must align with broader industrial goals. Moreover, our model is designed specifically for Industrial IoT applications, ensuring scalability and strong relevance to real-world manufacturing systems. Overall, the proposed approach transforms maintenance from a reactive or predictive task into a real-time, cost-effective, and system-wide optimization process, supporting the core objectives of Industry 4.0. Advanced methods such as LSTM, neural networks, and 1D-CNNs are widely used in predictive maintenance, especially when large volumes of IoT sensor data are available. However, our goal was to explore a simpler, and more interpretable approach. While polynomial regression may not always outperform these complex models in general, in our case, it achieved a high coefficient of determination (R\({}^{2}\) > 0.98), indicating an excellent fit to the observed data. This suggests that for the type and amount of data we used, the polynomial model is sufficient and effective. We believe that such a lightweight method can be beneficial in scenarios where computational efficiency and transparency are important. In our study, raw data for polynomial regression were not derived from IIoT sensor data, but rather from structured data corresponding to the following use cases:  
* the number of unnecessary maintenance operations as a function of the maintenance interval;
* the impact of maintenance period on the minimum and average value of system reliability;
* the relationship between the maintenance periods and the specific profit, depending on the maintenance cost and the income based on productivity;
* the relationship between expected system reliability and production hours covered by a predefined number of maintenance periods. We would like to provide a brief example to demonstrate hands-on insights into the application of our IIoT framework, as follows. For example, consider a flexible manufacturing system, where CNC milling machines are used, and they are equipped with several IIoT sensors to monitor their 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 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.