Gas-solid flow regime identification based on MFCC and HMM

Aiming at three common transitional flow regimes behind the detection and control devices in the pneumatic conveying pipeline,namely central flow,annular flow and stratified flow,the electrostatic flow noise signals were obtained through adopting an electrostatic sensor as the measuring equipment. With the speech signal processing method,the mel-frequency cepstrum coefficient(MFCC) and its first order difference of electrostatic flow noise signals were extracted as the feature parameters. In addition,the continuous Gaussian mixture hidden Markov model(CGHMM) was trained with the feature parameters,and the model libraries for different flow regimes were established. Then the extracted feature parameters were classified with the trained CGHMM model,and thus,the flow regimes identification was realized. The experimental results show that the identification rate of the proposed method reaches 98%,and a novel research method for the gas-solid flow regime identification as well as the pneumatic conveying detection and control devices is provided.