A novel noninvasive approach

based on flow-induced vibration

to the online flow regime identification for wet gas flow in a horizontal pipeline is proposed. Research into the flow-induced vibration response for the wet gas flow was conducted under the conditions of pipe diameter 50 mm

pressure from 0.25 MPa to 0.35 MPa

Lockhart-Martinelli parameter from 0.02 to 0.6

and gas Froude Number from 0.5 tO2.7. The flow-induced vibration signals were measured by a transducer installed on outside wall of pipe

and then the normalized energy features from different frequency bands in the vibration signals were extracted through 4-scale wavelet package transform. A "binary tree" multi-class support vector machine(MCSVM) classifier

with the normalized feature vector as inputs

and Gaussian radial basis function as kernel function

was developed to identify the three typical flow regimes including stratified wavy flow

annular mist flow

and slug flow for wet gas flow. The results show that the method can identify effectively flow regimes and its identification accuracy is about 93.3%. Comparing with the other classifiers

the MCSVM classifier has higher accuracy

especially under the case of small samples. The noninvasive measurement approach has great application prospect in online flow regime identification.