# Real-Time Maintenance Optimization with Industrial Internet of Things
## 4 Results
### IIoT Technologies-Supported Real-Time Optimization of Maintenance Operations
This implies that the higher the reliability requirement, the more consistent and shorter the maintenance intervals become. Furthermore, the rate of this reduction becomes increasingly steep once the minimum system reliability surpasses the 95% threshold, indicating a nonlinear relationship. In this critical range, even small increases in the reliability constraint cause disproportionately large decreases in the variability and length of the maintenance periods, highlighting the sensitivity of the system to high-reliability demands in real-time optimization scenarios. Figure 13 shows how the number of production hours covered by 20 maintenance periods changes as a result of real-time optimization simulation, depending on the expected system reliability within the range of 60% to 99%. The simulation demonstrates that as the expected reliability increases, the total production time that can be maintained without exceeding the reliability threshold generally decreases. This trend theoretically reflects the increasing need for more frequent or shorter maintenance intervals to preserve higher levels of reliability, if required. To describe the relationship between the expected system reliability and the number of production hours, a sixth-degree polynomial function provides a suitable fit. The coefficients of the polynomial regression were calculated using the least squares method based on the observed data, with the polynomial degree selected to balance  
Figure 12: The relationship between expected system reliability and statistical parameters of real-time optimized length of maintenance periods. model complexity and accuracy. The resulting R\({}^{2}\) value is greater than 0.98, confirming an excellent fit without signs of overfitting. In the next step, let us examine how the maintenance periods calculated by real-time optimization, based on dynamically changing real state data, affect the specific maintenance cost for a maintenance period, the specific income related to the productivity of the analyzed production system, and the specific profit depending on the maintenance cost and the income based on productivity. Figure 14 shows the results of this analysis in the case of \(r_{min}=0.6\), while Figure 15 shows the results in the case of \(r_{min}=0.95\). As Figure 14 shows, real-time optimization enables the development of maintenance periods that significantly contribute to maintaining a stable reliability of \(r_{min}=0.6\) within the production system. This is achieved by dynamically calculating the maintenance periods based on the real-time state information of the production system in real-time. Consequently, these periods are continuously adjusted within the analysis time window, reflecting the ever-changing state of the system. Figure 14: The impact of dynamic, real-time optimized maintenance periods on the specific maintenance cost for a maintenance period, the specific income related to the productivity of the analyzed production system, and the specific profit depending on the maintenance cost and the income based on productivity, in the case of \(r_{min}=0.6\). Figure 13: The relationship between expected system reliability and production hours covered by 20 maintenance periods. In this case, the total production time covered by 20 maintenance operations is 74.25 h, the total income is 30,983 USD, the total maintenance cost is 7529.5 USD, while the total profit is 23,454.5 USD. The hourly profit is 315.88 USD. Figure 15 shows the same results in the case of \(r_{min}=0.95\). In the case of \(r_{min}=0.95\), the total production time covered by 20 maintenance operations is 61.975 h, the total income is 38,987 USD, the total maintenance cost is 8744.7 USD, while the total profit is 30,242.3 USD. The hourly profit is 487.97 USD. This approach leads to a dynamically varying specific maintenance cost, as the timing and necessity of maintenance operations evolve alongside system conditions.