Multichannel Hybrid Parallel Classification Network Based on Siamese Network for the DAS Event Recognition System
Jiawen Wang, Sheng Huang, Chen Wang, Shuai Qu, Weitao Wang, Guangqiang Liu, Chunmei Yao, Na Wan, Xianggui Kong, Hong Ping Zhao, Shouling Liu, Ying Feng Shang, Gang‐Ding Peng · IEEE Sensors Journal · 2024
During the process of monitoring conveyor belt faults in underground coal mines, issues such as false alarms and low recognition rates arise. This was attributed to the extended monitoring distance of the belt conveyors, strong electromagnetic interference, and high environmental noise during the monitoring. Simultaneously, interference from other events can occur when a specific event is being monitored. To address this issue, this study proposes a Siamese recognition network based on a distributed acoustic sensor (DAS) system, featuring a multichannel hybrid parallel classification network (Si-MCHPCN). This network can accurately identify fault events even when they occur at different locations of the conveyor system. The experimental results show that an average accuracy of 94.8% can be achieved for the five event types. Compared to using the same feature extraction network, Si-MCHPCN improved the recognition accuracy by 9.5% and 11.6% compared to using a single-signal feature extraction network and by 36.7% compared to using a multichannel parallel classification network alone. Finally, after applying the Siamese network to the multichannel hybrid parallel classification network, not only did it improve recognition accuracy but it also resolved the issue of inaccurate data labeling, further enhancing the data reliability.