Design of Real-Time Monitoring and Fault Diagnosis System for Automation Equipment Driven By Edge Computing
Lili Pang
Corresponding author' E-mail: panglili_njit@163.com
Engineering Training Center, School of Applied Technology, Nanjing Institute of Technology; Nanjing， Jiangsu ，211167 ，China

Keywords: edge computing; automation equipment; real-time monitoring; fault diagnosis; system performance

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With the development of Industry 4.0, there is an urgent need to diagnose faults in automation equipment. This paper studies the monitoring and diagnosis system of automation equipment based on edge computing, and constructs a layered architecture including perception layer, edge layer, network layer and cloud layer. The edge layer adopts multi-threading, parallel computing and other technologies to improve the PLC data processing efficiency by 40%. The fault diagnosis algorithm based on dynamic weight optimization completes the weight update within 300 milliseconds, and the diagnosis accuracy is 12% higher than the traditional method. By setting up an experimental environment in real-time testing, the system has only a slight increase in delay under high data flow (1000 samples/second) and 4G network environment, which is significantly better than the traditional cloud architecture; reliability tests show that under complex working conditions, the fault diagnosis accuracy rate reaches 98.3%, which is higher than conventional algorithms such as BP neural network; compatibility tests show that the system has a high success rate of data processing on various brands of equipment. This system provides a reliable solution for monitoring and diagnosing industrial automation equipment, which is of great significance to improving the efficiency and stability of the manufacturing industry.