Search Results (3)

Aiming at the problems of high labor cost, low detection efficiency, and insufficient detection accuracy of traditional pipe gallery disease detection methods, this paper proposes a pipe gallery disease segmentation model, PipeU-NetX, based on deep learning technology. By introducing the innovative down-sampling module MD-U, up-sampling module SC-U, and feature fusion module FFM, the model optimizes the feature extraction and fusion process, reduces the loss of feature information, and realizes the accurate segmentation of the pipe gallery disease image. In comparison with U-Net, FCN, and Deeplabv3+ models, PipeU-NetX achieved the best PA, MPA, FWIoU, and MIoU, which were 99.15%, 92.66%, 98.34%, and 87.63%, respectively. Compared with the benchmark model U-Net, the MIoU and MPA of the PipeU-NetX model increased by 4.64% and 3.92%, respectively, and the number of parameters decreased by 23.71%. The detection speed increased by 22.1%. The PipeU-NetX model proposed in this paper shows the powerful ability of multi-scale feature extraction and defect area adaptive recognition and provides an effective solution for the intelligent monitoring of the pipe gallery environment and accurate disease segmentation. Full article

The widespread application of terminal sensors in power pipe galleries (PPGs) has significantly improved our ability to monitor power equipment status. However, the difficulties in battery replacement caused by confined space and energy loss caused by communication conflicts between sensors due to existing low-power communication strategies results in a lack of reliable energy supply for terminal sensors. In this context, a low-power communication strategy for terminal sensors is proposed. Firstly, a demand analysis is conducted on the status monitoring of PPGs, and a technical framework for low-power communication of terminal sensors is proposed. Afterward, a system model for the low-power communication of terminal sensors is established based on cognitive backscatter technology. Subsequently, key technologies, such as RF energy harvesting of terminal sensors and distance–energy level coupling analysis, are proposed to achieve continuous energy supply and avoid communication conflicts in the system model. Finally, a wireless communication simulation environment for PPGs is established to simulate the status monitoring process, based on terminal sensors, in order to verify the effectiveness of the proposed method. Full article

In this study, a numerical model of the cable cabin of a comprehensive pipe gallery was established to study the smoke flow diffusion behaviour of a comprehensive pipe gallery fire under a rectangular cross-section. The effects of fire source power (Q = 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 MW) and fire source location (D = 10, 20, 40, 50, 60, 80, 100 m) on the smoke flow characteristics—such as smoke layer height and thickness, longitudinal airflow velocity, and ceiling temperature distribution—were analysed, and the corresponding prediction model was fitted. The results show the following: (1) The height of the smoke layer decreases with increasing fire power, and the predictive model of the smoke layer thickness obtained from the fitting is proportional to the smoke mass flow rate and inversely proportional to the aspect ratio of the pipe gallery. (2) Longitudinal air velocity prediction models of D < 50 m and D ≥ 50 m are fitted, and the average error between them and the numerical simulation values is 9.611%. (3) The temperature decay gradient of the smoke decreases gradually with increasing distance from the fire source, while there is a significant temperature difference between the two sides of the fire source. The average relative errors of the dimensionless temperature rise models fitted upstream and downstream of the fire source in the form of $\frac{\Delta T}{T_0}=A{e}^{B\left(\frac{D-X}{H}\right)+C}$ exponentials with respect to the numerical simulations were 11.688% and 7.296%, respectively. The results of the study can provide a reference for smoke flow and fire prevention and control in comprehensive pipe galleries. Full article