A Gaussian mixture model based virtual sample generation approach for small datasets in industrial processes

Abstract

Due to small-quantity and often imbalance of labeled samples, it is challenging to establish a robust and accurate prediction model through data-driven methods. To deal with the small dataset problem, new virtual samples may be generated via virtual sample generation (VSG) methods based on the trend of the original small raw dataset, thereby improving modeling performance. Effective VSG is desirable, but also challenging. Conventional VSG usually assumes that the raw sample set contains only a single operating mode. Taking multi-mode into account will improve the VSG based modeling performance since actual processes are often multi-mode. To this end, an information expansion function considering sample density and amount (IEDA) is first developed to expand the domain range of the attributes in this paper. Then, virtual samples under the multiple operating mode condition are generated by proposing a Gaussian mixture model based virtual sample generation (GMMVSG) method. Applications of GMMVSG on Tennessee Eastman benchmark process and an industrial hydrocracking process show significant improvement of modeling and predictions over other conventional VSG methods.