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

To verify the effectiveness of the IoT-based BMU remote monitoring and fault diagnosis system proposed in this paper (see Section 2 for details), especially the performance of its \begin{table}
\begin{tabular}{c c c} \hline
**Item** & **Quantity** & **Description** \\ \hline Simulation control system of the BMU & 1 & The control system simulating the working state of the BMU. \\ Communication module & 1 & The gateway to transfer data to the cloud server. \\ Cloud server & 1 & The platform for providing cloud services. \\ Laptop & 1 & The device to log in to the client for monitoring. \\ \hline \end{tabular}
\end{table} 
Table 2: Key elements of the test system. Figure 5: Simulation control system of the BMU and its main parts. fault diagnosis model, in this study, a simulation control system is established for the BMU and cloud server, and we conducted a series of simulation experiments. Considering the various operating conditions and fault modes that may occur during actual BMU operation (refer to Table 1), the experiment used a simulated dataset containing normal operating conditions and multiple typical fault types to obtain real-time data of the BMU simulation control system at a frequency of 2 s. Figure 6a demonstrates the monitoring interface during normal BMU operation (executing the boom left-rotation maneuver). After inputting the fault samples of the left-rotation contactor into the system, the client displays the interface shown in Figure 6b. ### Result Analysis

The simulation dataset is generated by the BMU testing system, which contains six categories of states, covering normal operation, and five typical BMU faults that are selected according to Table 1:

Category 0: Normal Operation

Category 1: Host Communication Abnormality

Figure 6: (a) BMU operating CM. (b) The diagnosis results displayed in the information window of the client. Figure 7 shows the confusion matrix between the predicted category of the model and the true category of the sample in the simulated diagnosis. From Figure 7, it can be seen that the values of the main diagonal elements of the matrix are much higher than those of the non-diagonal elements, indicating that the model has very high recognition accuracy for all six categories. For example, for categories such as \"normal operation\" and \"contactor failure\", the vast majority of samples are correctly classified. A small number of values on non-diagonal lines reveal confusion in the model. A small number of samples in category 4 were incorrectly identified as category 1, revealing a certain similarity in the selected features between these two types of faults. Overall, the diagnostic accuracy reached 97%, verifying both the feasibility and effectiveness of this diagnostic framework. #### 3.3.2 Feature Effectiveness Analysis

To comprehensively evaluate the capability of the selected feature set in distinguishing different fault categories, t-distributed Stochastic Neighbor Embedding (t-SNE) was employed to reduce the dimensionality of the high-dimensional feature data, and the visualization results in the 2D space are presented in Figure 8.

Figure 7: Confusion matrix. In Figure 8, each point represents a data sample, and the color corresponds to its true category label. It can be observed that the sample points of different categories tend to form relatively independent clusters in the two-dimensional space. The cluster of \"normal operation\" (blue dots) has good discrimination from the clusters of other fault categories. Most fault categories, such as \"contactor failure\" and \"rope slack\", have also formed unique clustering areas. Overall, the t-SNE visualization results indicate that based on the selected features in this paper, different fault categories and normal states have good separability in the feature space, which further supports the conclusion that the proposed diagnostic model can achieve high accuracy.