Automatic online quality monitoring and prediction model updating method for rubber hardness

Abstract

The invention discloses an automatic online quality monitoring and prediction model updating method for rubber hardness. The method includes: setting up an online hardness prediction model according to a training data set, and computing an alarm limit, a warning limit and a selection limit; inputting a new rheological parameter sample into the online hardness prediction model to automatic output rubber hardness values; and determining whether to update the online hardness prediction model or enable a quality monitoring system to alarm according to the relation among the Q statistics value of the new rheological parameter sample Xnew, the alarm limit, the warning limit and the selection limit, and allowing an operator to check operating conditions of a production line to make corresponding technical adjustment to recover the current operating condition to normal. Precise rubber hardness values are obtained by continuously updating the model, rubber quality is improved, higher precision is achieved, and potential safety risks are reduced. Further, computer memory space is reduced along with reduction of sample numbers, and computing speed is evidently increased.

Description

technical field [0001] The invention relates to an on-line quality monitoring method in the field of rubber tire manufacturing, in particular to an on-line automatic quality monitoring of rubber hardness and a method for updating a prediction model. Background technique [0002] In recent years, with the continuous increase of the gross national product, the rubber industry has developed by leaps and bounds, coupled with the continuous development of China's automobile industry, it has played a huge role in promoting the rubber industry. No matter in the field of production or life, more and more rubber products are used. With the continuous emergence of new products and the continuous expansion of application fields, newer and higher requirements are put forward for the production of rubber products. [0003] With the development of large-scale industrial processes, people have higher and higher requirements for quality control processes, especially for time-varying system...

AI Technical Summary

Problems solved by technology

[0005] A big defect of the current data-driven method is that it cannot automatically update the model in real time, especially in the time-varying process, which will seriously affect the prediction accuracy of rubber hardness, resulting in errors in the measurement of rubber hardness and reducing the rubber hardness. Quality and life; once the statistic (Q statistic) of the traditional monitoring algorithm is established, it cannot be updated online, that is, as the industrial process progresses, the interpretation of the original model to the industrial parameters will become worse and worse, so its time-varying The tracking ability is gradually weakened, and the system with significant time-varying characteristics such as intermittent processes cannot be effectively monitored online

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