Passive acoustic identification of bubble flow regime based on synchrosqueezing wavelet transform and deep learning

Abstract

A passive acoustic method based on synchrosqueezing wavelet transform (SWT) and deep learning was proposed to automatically identify bubble flow regimes in process industries. The method was established on the bijection relationship between bubble flow regime and the time-frequency (TF) textures of its passive acoustic emission (PAE) signals. Specifically, the PAE signal of the bubble flow was first acquired by hydrophones, then converted into TF representations (TFRs) by SWT and finally used to train convolutional neural network (CNN) to identify bubble flow regimes automatically. The effects of TF analysis method and CNN architecture were studied: the SWT represented TF textures of PAE signals the best, and its combination with ResNet gave the highest identification accuracy of 99.67%. As its main contribution, the method validated the feasibility of PAE and CNN in bubble flow regime identification, and achieved automatic identification of bubble flow regime with high accuracy and barely no subjective interference.