An Improved Generalized-Trend-Diffusion-Based Data Imputation for Steel Industry

Abstract EN

Integrality and validity of industrial data are the fundamental factors in the domain of data-driven modeling.

Aiming at the data missing problem of gas flow in steel industry, an improved Generalized-Trend-Diffusion (iGTD) algorithm is proposed in this study, where in particular it considers the sort of problem with data properties of consecutively missing and small samples.

And, the imputation accuracy can be greatly increased by the proposed Gaussian membership-based GTD which expands the useful knowledge of data samples.

In addition, the imputation order is further discussed to enhance the sequential forecasting accuracy of gas flow.

To verify the effectiveness of the proposed method, a series of experiments that consists of three categories of data features in the gas system is presented, and the results indicate that this method is comprehensively better for the imputation of the periodical-like data and the time-series-like data.