Zero-Shot State Identification of Industrial Gas–Liquid Two-Phase Flow via Supervised Deep Slow and Steady Feature Analysis

Gas–liquid two-phase flow is a complex dynamic and nonlinear process that is widely encountered in many process industries. Accurate flow state identification is crucial for ensuring operation safety and economic benefits. However, obtaining training samples for certain flow states can be difficult due to safety requirements and high costs. Therefore, a zero-shot learning (ZSL) based flow state identification strategy is proposed from the perspective of attribute description and attribute transfer, in which the common attribute space is constructed by the semantic description of flow state categories. The attribute-relevant features are extracted by the proposed supervised deep slow and steady feature analysis (SD-S$^{\mathbf{2}}$FA) under the supervision of attributes. In SD-S$^{\mathbf{2}}$FA, an extended Siamese network is designed to extract slow and steady features (S$^{\mathbf{2}}$ Fs), in which three 1D convolutional neural networks (1D-CNN) represent the nonlinear feature embedding function, and the Siamese architecture can capture the long-term temporal coherence. Since the state attributes are shared by all the flow states, the identification for unseen flow states can be realized by attribute prediction and attribute transfer. The effectiveness and superiority of the proposed method is demonstrated through the gas–liquid two-phase flow experiment.