# Remote Monitoring and Diagnosis for Building Maintenance Units Based on Internet of Things System
## 2 System Design and Application
### Overall System Architecture Design
#### 3.3.3 Stability Analysis

To evaluate the long-term operational reliability of the system, a 7-day continuous simulation test was conducted. The stability of its connection is shown in Figure 9a, and based on this, the performance stability of the diagnostic model during long-term operation was further evaluated. In the test, the 7-day simulation process was divided into seven time windows, and the comprehensive recognition accuracy of the diagnostic model for all states (normal and various types of faults) within each window was independently calculated. The result is shown in Figure 9b. The system connection status shown in Figure 9a indicates that the system maintained a high degree of connection stability during the testing period, proving its reliability as the foundation of the remote monitoring platform. From Figure 9b, it can be seen that although there was performance degradation throughout the entire 7-day testing cycle, the diagnostic accuracy of each time window remained stable at over 95%, indicating that the diagnostic model proposed in this paper not only has high overall accuracy but also exhibits good performance stability under simulated continuous operating conditions. Combining the results of Figure 9a,b, it has been verified that the system can stably transmit data and continuously provide high-precision state analysis and fault diagnosis. Figure 8: t-SNE visualization of the feature space. ## 4 Conclusions

In summary, based on the IoT architecture, this article has achieved the acquisition and storage of complex multi-source data for BMUs. On this basis, the system integrates expert-system prior knowledge with real-time operational data, establishing multi-source relational association rules based on operational data and fault samples, thereby enhancing its real-time CM and fault diagnosis capabilities. An integrated test system with multiple BMUs was constructed, and the test dataset extracted from it was used to evaluate the system's real-time fault recognition rate, achieving 97% accuracy as measured by a confusion matrix. Furthermore, simulated continuous operation tests were conducted to assess the system's stability in analyzing BMU operational and fault states. The development of this system not only enhances the efficiency and accuracy of real-time CM and fault diagnosis for BMUs but also lays a solid foundation for the practical implementation of intelligent maintenance in BMU applications. The real-time fault diagnosis method, which preprocesses complex multi-source data relationships of BMUs through an IoT architecture and integrates expert-system prior knowledge with real-time operational data, provides novel technical approaches and solutions for this field. While the system has achieved its initial objectives, certain limitations remain. The system could be enhanced through two key aspects:

1. Although this study proposes IoT-based data collection to address insufficient samples, the process remains time-consuming. Future improvements could incorporate evolutionary learning mechanisms and data augmentation techniques to expand the sample size, thereby obtaining more accurate real-time data and refining the fault diagnosis correlation model. Figure 9: (**a**) Simulated system connectivity over 7 days. (**b**) Simulated diagnosis accuracy over 7 days. 2. The current fault diagnosis method focuses on outputting real-time status results and root cause analysis for BMU but lacks preventive recommendations. The system could integrate multi-axis force sensors and machine learning algorithms to analyze structural wear, enabling predictive maintenance strategies and establishing a comprehensive intelligent maintenance framework for BMUs. Validation, R.S. ; Data curation, K.L. ; Writing--original draft, B.D. ; Writing--review & editing, B.D., K.L., C.L. and R.S. ; Project administration, C.L. ; Funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript. This work was financially supported by the National Natural Science Foundation of China (No.