Abstract: W. Wu, S. Zhang, F. Dong (2022)

Proceedings of the IEEE Instrumentation and Measurement Technology Conference (I2MTC 2022), (), p. (2022) DOI:10.1109/I2MTC48687.2022.9806542

Gas-water Two-phase Flow Status Monitoring based on Multi-sensor Signals and CA-PCA Strategy

The fluid structure of gas-water two-phase flow is complex and diverse, and the flow status changes with time. Monitoring the evolution process of flow status in time and accurately is of practical significance for the stable operation of industrial process. With the development of multi-sensor technology, a variety of sensors designed based on different sensitivity principles can obtain comprehensive information of flow process. However, due to the uncertain factors such as the change of environment and the randomness of fluid flow, there are both nonstationary and stationary signals. In this paper, Augmented Dickey-Fuller (ADF) test is used to distinguish the nonstationary signals and the stationary signals from the multi-sensor measurement data of gas-water two-phase flow in horizontal pipe. The long-term equilibrium relationship of nonstationary signals is processed by cointegration analysis (CA) method. It solves the problem that the fluctuation of nonstationary signals masks the change trend of flow status characteristics. The nonstationary signal monitoring index is established based on the obtained cointegration sequence. Principal component analysis (PCA) method is used to process the stationary signals, which obtains the principal characteristics and residuals of flow status to establish the stationary signal monitoring indexes. Considering the non-stationarity and stationarity of gas-water two-phase flow process signals, the three monitoring indexes are integrated through Bayesian inference to realize flow status monitoring. The CA-PCA monitoring strategy is applied to the gas-water two-phase flow data to verify the feasibility and effectiveness.

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