The aim of this study is to address issues such as the large volume of realtime measurement data for pipeline vibrations, prolonged transmission delays, and wastage of computational resources. By adopting edge computing theory, the data processing steps are moved closer to the devices, thereby accelerating the speed of status monitoring and optimizing the utilization of computational resources. This study provides a detailed overview of the overall functional framework, hardware design methodology, and vibration signal conversion algorithm of the edge computing perception system. The system consists of two parts: edge computing devices and data aggregation devices. The former is positioned at mechanical vibration sources for realtime analysis and processing of extensive redundant data, while the latter communicate with multiple edge computing devices via wireless signals to project aggregated information to maintenance terminals. Using a phaseshifting camera pipeline vibration experiment as a case study, this research demonstrates that the structural vibration perception technology based on edge computing can accurately identify abnormal vibration phenomena in the pipeline at frequencies of 64 Hz, 115 Hz, and 279 Hz. By guiding the use of vibration dampers, the study achieved a significant reduction in the pipeline′s maximum vibration amplitude from 068 m·s-1 to 00016 m·s-1, showcasing its substantial engineering practical value.