As a highly complex and time-varying process, gas-water two-phase flow is commonly encountered in industries. It has a variety of typical flow states and transition flow states. Accurate identification and monitoring of flow states is not only beneficial to further study of two-phase flow but also helpful for stable operation and economic efficiency of process industry. Combining canonical variate analysis (CVA) and Gaussian mixture model (GMM), a strategy called multi-CVA-GMM is proposed for flow state monitoring in gas-water two-phase flow. CVA is used to extract flow state features from the perspective of correlation between historical data and future data, which solves the cross correlation and temporal correlation of multi-sensor measurement data. GMM calculates the possibility that the current flow state belongs to each typical flow pattern and judges the current flow state by probability indicators. It is conducive to follow-up use of Bayesian inference probability and Mahalanobis distance-based (BID) indicator for flow state monitoring, which avoids repeated traversal of multiple CVA-GMM models and improves the efficiency of the monitoring process. The probability indicators can also be used to analyze transition flow states. The method combining the probabilistic idea of GMM with the deterministic idea of multimodal modeling can accurately identify the current flow state and effectively monitor the evolution of flow state. The multi-CVA-GMM method is validated by using the measured data of the horizontal flow loop of gas-water two-phase flow experimental facility, and its effectiveness is proved.

作为高度复杂且随时间变化的过程，气水两相流在工业中通常会遇到。它具有各种典型的流动状态和过渡流动状态。准确识别和监测流态，不仅有利于两相流的进一步研究，而且有利于过程工业的稳定运行和经济效益。结合规范变量分析（CVA）和高斯混合模型（GMM），提出了一种称为多CVA-GMM的策略，用于气-水两相流的状态监测。从历史数据和未来数据之间的相关性角度出发，使用CVA提取流量状态特征，解决了多传感器测量数据的互相关性和时间相关性。GMM计算当前流动状态属于每个典型流动模式的可能性，并通过概率指标判断当前流动状态。这有利于后续使用贝叶斯推断概率和基于Mahalanobis距离（BID）指标进行流状态监测，从而避免了重复遍历多个CVA-GMM模型并提高了监测过程的效率。概率指标还可以用于分析过渡流状态。该方法将GMM的概率思想与多模式建模的确定性思想相结合，可以准确地识别当前的流动状态，并有效地监测流动状态的演变。利用气水两相流实验装置水平流回路的测量数据验证了多CVA-GMM方法的有效性，