# Real-Time Maintenance Optimization with Industrial Internet of Things
## 6 Conclusions  
The model is a general framework that, due to its recursive nature, can be applied to a wide range of system structures. Its flexibility allows it to be used in various cases regardless of the specific configuration of components. However, systems with bridge-type connections may represent an exception, as these structures can introduce dependencies that are not fully captured by the current recursive logic. Nonetheless, for most conventional system architectures, the model remains broadly applicable and effective.  
This research introduces a novel real-time maintenance optimization framework that moves beyond traditional fault detection by integrating Industrial IoT data with adaptive, model-driven decision-making. Unlike previous studies, which focus primarily on monitoring or anomaly detection, our approach continuously adjusts maintenance strategies in real time to minimize costs and maximize system efficiency. A key innovation lies in transforming maintenance from a reactive or predictive task into a strategic, real-time optimization process that aligns with the broader objectives of Industry 4.0. The recursive and iterative structure of the model enables scalable application across diverse system architectures, offering a generalized solution suitable for smart factory environments. By embedding real-time optimization into safety assessment and risk management, the research enhances the responsiveness, reliability, and overall intelligence of maintenance operations in industrial systems.  
Safety assessment from a maintenance perspective is a fundamental element of industrial risk management. It involves the systematic identification of potential hazards related to maintenance activities, such as equipment failures, human errors, or exposure to hazardous substances. Risk assessment quantifies the likelihood and consequences of these hazards, enabling the prioritization of preventive actions. Our proposed real-time maintenance optimization plays a key role in this process by supporting data-driven decision-making, allowing for the early detection of risks and rapid response. This approach significantly enhances the safety, reliability, and efficiency of maintenance operations.  
Future research could explore the integration of real-time maintenance optimization with dynamic production scheduling to improve overall system coordination. Another promising direction is the development of multi-objective optimization models that balance cost, energy use, environmental impact, and safety. Enhancing the system's ability to learn from past maintenance outcomes could improve decision-making over time. Finally, research on cybersecurity and data integrity will be essential to ensure the safe and reliable use of IIoT in industrial environments.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are unavailable due to privacy restrictions. Conflicts of Interest: The authors declare no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript: AMQP Advanced Message Queuing Protocol API Application Programming Interface DRP Degradation Rate Parameter GA Genetic Algorithm IIoT Industrial Internet of Things KPI Key Performance Indicator LSTM Long Short-Term Memory MQTT Message Queuing Telemetry Transport 1D-CNN One-Dimensional Convolutional Neural Network