The complexity of natural gas production processes and the variety of parameters and working conditions makes it difficult to identify abnormalities of operation processes. To address this problem, we proposed an anomaly identification method for natural gas dewatering process based on adaptive Gaussian mixture model (AGMM) and principal component analysis (PCA), namely anomaly identification for multi-case time-varying data (AIMT). AIMT uses sliding-window PCA to update the master metamodel of the monitoring data for the description of the working process based on AGMM. Adaptive optimization of the parameters of each Gaussian model in AGMM is carried out through a large amount of historical monitoring data in order to achieve effective characterization of complex multiple operating conditions. The identification of process anomalies is realized by squared prediction error (SPE) and Hotelling's T2 (T2). The effectiveness of the method proposed in this paper has been verified by data from single condition, variable condition, and multiple condition operations and real failure events in an expanded 1 million triethylene glycol (TEG) dewatering equipment at Chongqing Gas Field, PetroChina Southwest Oil and Gas Field Company.