The knowledge of flow regime is very important for quantifying the pressure drop

the stability and safety of two-phase flow systems.Based on image multi-feature fusion and support vector machine

a new method to identify flow regime in two-phase flow was presented.Firstly

gas-liquid two-phase flow images including bubbly flow

plug flow

slug flow

stratified flow

wavy flow

annular flow and mist flow were captured by digital high speed video systems in the horizontal tube.The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques.To improve the performance of a multiple classifier system

the rough sets theory was used for reducing the inessential factors.Furthermore

the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples

and the flow regime intelligent identification was realized.The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes

and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-iquid two-phase flow in the horizontal tube.Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow

and achieved higher identification ability than that of single characteristic.The overall identification accuracy was 100%

and an estimate of the image processing time was 8 ms for online flow regime identification.