Experimental study on online monitoring of fluid flow state in gas pipelines based on statistical slow feature analysis

Abstract

Abstract: Gas pipelines often face complex transportation environment conditions,which may lead to the accumulation of liquid in the pipeline,affecting the transportation efficiency and safety. Aiming at the fluid state monitoring in gas pipelines,a non-invasive measurement technology based ultrasonic Lamb waves is utilized,and a statistical slow feature analysis （SSFA） method is proposed. Firstly,the ultrasonic signal is processed by sliding windows to construct the statistical pattern matrix,which extracts the low-order and high-order statistical information. Then,a slow feature analysis （SFA） model is established to extract the features of the statistical pattern matrix,mining the dynamic information of process changes. Besides,the monitoring statistics are calculated based on slow features. Finally,Bayesian inference is used to make decision fusion of monitoring statistics to provide intuitive and accurate monitoring indicators. Experiment results show that SSFA method can effectively use ultrasonic Lamb wave signal to accurately monitor the fluid state in gas pipelines,and its accuracy is the highest compared with other methods.

Key words: gas pipeline, ultrasonic Lamb wave, statistic pattern, slow feature analysis, state monitoring

CLC Number:

TE832

LIU Jinhai, WANG Junfeng, LIU Xuetao, WANG Luyao, LI Linghan, LIANG Guanghui. Experimental study on online monitoring of fluid flow state in gas pipelines based on statistical slow feature analysis[J]. TIANJIN SCIENCE & TECHNOLOGY, 2025, 52(05): 5-10.

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