A Step-by-Step Reduction Method for Screening Sensitive Variables Affecting Product Quality in Complex Industrial Processes

Abstract

The invention discloses a method for screening sensitive variables that affect product quality in complex industrial processes based on step-by-step reduction, which belongs to the field of soft measurement technology and includes the following steps: selecting auxiliary variables that affect product quality through expert knowledge and collecting data samples ;Comprehensively consider the variable correlation and the sensitivity of the variable to the change of working conditions to calculate the auxiliary variable sensitivity index, and initially screen the sensitive variables that affect the leading variable; build a weighted cosine Martin system to accurately screen the key sensitive variables that affect product quality. The present invention can accurately reflect the correlation of variables and working condition information, and at the same time better reduce the redundancy of variables, not only can improve the prediction accuracy of product quality, but also can effectively reduce the complexity of the prediction model, and the maintenance of the soft sensor model equally significant.

Description

technical field [0001] The invention relates to the technical field of soft measurement, in particular to a method for screening sensitive variables affecting product quality based on step-by-step reduction. Background technique [0002] With the development of advanced manufacturing technology, the manufacturing industry has put forward higher requirements for production development from the expansion of quantity and scale to the improvement of quality, efficiency and environmental protection. In order to be able to monitor and evaluate the process operation status in a timely and effective manner, to realize accurate diagnosis of system faults and fast tracking of product quality, it is necessary to conduct real-time detection of process-critical product quality. However, due to the harshness of the testing environment, the high cost of analytical instruments, and the hysteresis of laboratory analysis, it is currently difficult to achieve online testing for the quality of ...

AI Technical Summary

Problems solved by technology

The filtering variable selection method does not depend on the learning algorithm, and adapts the learning algorithm by changing the data, but this method tends to ignore the variable correlation, resulting in the selected subset may not be the optimal subset

[0005] In order to solve the problem of variable redundancy in the filtering variable selection method, many attempts and researches have been carried out in the theoretical circles and industrial practices at home and abroad. These studies can effectively solve the problem that the filtering variable selection method tends to ignore the correlation and redundancy between variables. , but does not have the ability to describe the process working condition information

However, in the actual industrial production process, affected by fluctuations in the quality of imported raw materials, adjustments in processing schemes, and changes in product specification requirements, the production conditions are in a state of fluctuation, and there will be certain differences in product quality under different conditions.

If the selected auxiliary variables cannot describe the changes of working conditions well, the accuracy of the prediction model will be reduced to a certain extent.

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