Production line quality monitoring method, device and system

Abstract

The invention provides a production line quality monitoring method, device and system. The method comprises the steps that target monitoring parameters of all equipment units in a production line areacquired; inputting the target monitoring parameters into a trained first neural network model, and determining fault state information of each equipment unit through the first neural network model; and inputting the fault state information of each equipment unit into a trained second neural network model, and determining the overall fault state of the production line through the second neural network model. The quality of the production line is monitored and judged in an artificial intelligence algorithm mode and can be monitored in an automatic mode, the efficiency of monitoring the qualityof the production line is greatly improved, and the fault state of each equipment unit of the production line and the overall fault state of the production line can be judged in real time.

Description

technical field [0001] The invention relates to the technical field of industrial monitoring, in particular to a production line quality monitoring method, device and system. Background technique [0002] my country has a complete range of industrial products, and its production capacity ranks first in the world. However, there is still a big gap between product quality and developed countries in Europe and the United States. The problem of low quality has not been effectively resolved to a certain extent. [0003] In the process of product manufacturing, there are many factors that affect product quality, covering multiple stages such as R&D and design, raw materials, processing technology, use and maintenance, among which the quality stability and consistency of the production process is a major problem faced by my country's manufacturing industry One of the key links affecting this problem is whether the production line as a whole runs in an orderly and stable manner. Tha...

AI Technical Summary

Problems solved by technology

However, due to the many factors affecting the actual industrial environment and the changes are difficult to judge, these manual methods are not only inefficient, but also inaccurate, and at the same time cannot meet the real-time monitoring of the quality of the production line to ensure the quality of the production line itself

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