Soft measurement modeling method based on modal common feature separation

Abstract

The invention relates to a soft measurement modeling method based on modal common feature separation. According to the method, data characteristics of an industrial process are fully considered, modal common characteristics of the multi-modal industrial process are extracted through a beta variational auto-encoder and a gradient inversion method, meanwhile, modal unique coefficients are generated through input, a modal unique system and the modal common characteristics are multiplied, and the method is used for soft measurement modeling of the multi-modal process. And combining the modal basic characteristics with modal unique parameters for regression modeling of soft measurement, and estimating and forecasting key quality variables. Compared with other existing methods, the method has the advantages that the multi-modal and non-linear characteristics in the industrial data can be effectively extracted, automatic modal division is performed on the to-be-measured multi-modal input data, modeling is performed by utilizing common characteristics and unique characteristics of modals, and the prediction precision of the soft measurement model on the multi-modal industrial process is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of industrial process control, and relates to a soft measurement modeling method based on modal shared feature separation. Background technique [0002] In industrial processes, the key variables used to aid in process monitoring, fault diagnosis, and quality prediction are called quality variables, while sensor data that is easily collected in the process is called process variables. However, due to the limitation of some objective conditions, quality variables are often difficult to obtain directly, for example, the measurement facilities are extremely expensive, the measurement environment is very difficult, and the laboratory analysis delay is relatively large. For such key quality variables that are difficult to directly observe, generally, the soft sensing modeling method can be used, that is, to construct the mathematical relationship between the process variables that are easy to measure and the qual...

AI Technical Summary

Problems solved by technology

This requires the introduction of a deeper network structure or the addition of network nodes for nonlinear fitting. The calculation scale is large and the time cost is high. It is difficult to apply to the online monitoring process with high real-time requirements; the adaptive learning method can continuously update the model in real time. Thus ignoring the multimodal characteristics of the process, this method has a better modeling effect for some simple multimodal processes, but when dealing with complex nonlinear multimodal processes, serious information loss problems will occur

[0004] In summary, some existing solutions to multimodal problems do not fully consider the data characteristics of industrial processes

Images