Gas-Water Two-Phase Flow Status Identification Based on Jointly Specific and Shared Dictionary Learning

Gas-water two-phase flow is a complex and changeable process, which widely exists in a variety of industrial scenarios. The gas-water two-phase flow has complex flow characteristics and fluid structure, which can be changed into different flow statuses. There are both differences and similarities among various flow statuses. In order to realize the accurate identification of gas-water two-phase flow, a jointly specific and shared dictionary learning (JSSDL) method is proposed. Based on multi-sensor data, the specific discrimination dictionary for different flow statuses and the shared dictionary for similar characteristics are established respectively to solve the problem of gas-water two-phase flow status identification. In the horizontal gas-water two-phase flow circuit, the effectiveness of the method is verified by experiments.