Research on the Design of Edge Intelligent Acquisition System for Equipment Health Management

Predictive maintenance is of great significance for the health management of process industry equipment. Companies in sectors like metallurgy and coking frequently run a sizable number of key industrial assets. Continuous monitoring is necessary to guarantee these assets operate steadily. In order to decrease the amount of data transmitted and improve the responsiveness of data acquisition, it is essential to flexibly design and combine the acquisition techniques based on the various rotational speeds of the equipment. The dynamic signal acquisition system used for equipment health management has been designed and researched in this work in response to these needs. The following is the specific content: The collector's hardware structure and topology were examined first, followed by the design of the edge data acquisition procedure and method, and ultimately, the application and verification were carried out in a real factory. According to the research findings, the designed data collector and acquisition system enables precise data collection in complex environments. Its edge computing capabilities effectively reduce data transmission latency and alleviate network bandwidth pressure. This study offers a practical and convenient technical support for implementing predictive maintenance and ensuring safe equipment operation, as well as an efficient data acquisition method for equipment health management in industrial enterprises like coking and metallurgical plants.