## 1 Introduction
By importing two-phase flow conductivity signals into the bidirectional extended short-term memory network and CNN, considerable signal details were used to understand the depth characteristics of distinct flow patterns. The results show that the model can accurately identify complex flow patterns. The aforementioned methods only use data from a single sensor and feature extraction of only one source of information, and do not fully utilize the rich understanding of the flow patterns in order to identify the feature fusion flow pattern. This can affect generalization performance of the recognition method, which means it is not fully applicable to different flow patterns. Therefore, the present study uses multi-sensor information fusion (MSIF) for flow pattern recognition to improve the model generalization performance. Multi-sensor information fusion (MSIF) is widely used in the military [11], medical field [12], fault diagnosis [13], intelligent transportation [14], and many other applications. For flow pattern identification, [PERSON] et al. [15] combined X-ray chromatography imaging and capacitance chromatography imaging, and fused the acquired data to understand and study the gas-liquid distribution of the absorption column at the experimental monitoring point. [PERSON] et al. [16] used a combination of capacitance tomography and \(\gamma\)-imaging systems in their oil-gas-water three-phase flow rate detection study. Combining the two modalities, three-phase rate and flow pattern were obtained. Up to now, data-level fusion, feature-level fusion, and decision-level fusion are the three main focuses of MSIF technology. Data-level fusion directly combines the original data from many sensors. Then feature vectors are extracted from the fusion data set for training and recognition, which requires two kinds of data from the same type of sensors and is computationally intensive. Decision-level fusion processes the data collected by all sensors for recognition, fuses the recognition results, and outputs the total decision results. It is greatly affected by the measurement target and has a high processing cost and the vast elements of the knowledge base. Feature-level fusion (Figure 2) extracts features from the original data measured by each sensor, and then fuses the features extracted by each sensor into a feature vector to identify the target state. Feature-level fusion technology is the most mature among the three levels of fusion. Since a complete set of proven feature association techniques has been established in feature fusion, the consistency of fused information can be guaranteed. Therefore, feature fusion is chosen as the mode of MSIF in the present research. Deep learning has strong learning ability and good adaptability, and the disadvantages of single sensor testing simplex and incomplete information can be solved by multi-sensor information fusion technology. A convolutional neural network can automatically optimize features, remove noise, and obtain features. Therefore, the selected approach for the present work is based on CNN and feature fusion to identify the gas-liquid two-phase flow pattern of a subsea M-shaped rigid jumper. At the same time, the features of the flow vibration signal and capacitance chromatography signal are fused by correlation function. First, the experiment was conducted based on the multiphase flow experimental platform of Dalian Maritime University. Then a vibration signal acquisition system and a tomographic imaging acquisition system were built. Gas-liquid two-phase flow experiments were conducted simultaneously to obtain ECT image data sets and vibration signal data sets for four typical flow types: stratified flow, slug flow, annular flow, and bubbly flow. The  
Figure 2: Feature-level fusion. convolution neural network structures suitable for signals and images were built to extract the features of two different signals, and the feature fusion was carried out at the complete connection layer. Finally, the integrated features were trained and classified.