A Novel PCA-Based Slug Flow Characterization Using Acoustic Sensing

In a modern industrial process, the accurate measurement of two-phase flow signals is of vital importance for various purposes including condition-based monitoring of pipelines, the avoidance of costly material and environmental damages, and danger to the operational personnel. In this work, a novel blind approach for denoising two-phase liquid and gas flow in a horizontal liquid-gas pipe is proposed. This approach employs the structured signal subspace (SSS) method on the multichannel signal acquired from the transducers connected to the wall of the pipe. The proposed approach utilizes only the output observations from the sensor, i.e, the recorded signal, and does not require any knowledge of the input signal or the channel. The multichannel signals recorded are first pre-processed using the principal component analysis (PCA) and then the blind SSS method is used to denoise the input signal before estimating it. The numerical results showed that the proposed algorithm outperforms the state-of-the-art algorithms (SOTA) which includes the eigen value decoposition (EGD)-based method and the independent component analysis (ICA)-based method, while the proposed PCA-SSS method achieved a performance of $-22.7dB$ in the presence of Gaussian noise, the EGD and ICA acheived $-16.39dB$ and $-18.09dB$ , respectively, showing the superiority of the proposed method. Similar analysis were performed in the presence of a non-Gaussian noise and colored noise and the proposed algorithm also outperformed the other methods. Hence, the PCA-SSS method can be used for a better characterization of a slug flow regime by exploiting the Toeplitz structure embedded in the signal vector acquired from the array of sensors via the communication model for denoising the two-phase flow, and does not rely on the knowledge of the input signal vector.