Latent temporal feature dual‐head coordination network for industrial process soft sensing

Abstract To address the significant temporal characteristics and the scarcity of labelled data in complex industrial processes, specifically targeting the latent temporal dynamics that are difficult to observe directly during process evolution, this paper proposes a latent temporal feature dual‐head coordination network (LTFDHCN). First, a posterior generation (PG) module is constructed, utilizing a bi‐directional long short‐term memory (Bi‐LSTM) network to extract bidirectional temporal context and infer the posterior distribution in the latent space. Second, a prior prediction (PP) module is designed to establish a dynamic temporal model based on the latent variable of the previous time step. This mechanism predicts the prior distribution for the current moment, breaking the traditional assumption in variational autoencoders (VAE) that latent variables follow a static prior, thereby capturing deeper latent temporal dynamics. The Kullback–Leibler (KL) divergence is employed to minimize the discrepancy between the posterior and dynamic prior distributions, enforcing the latent space to encode temporal dependencies. Furthermore, a dual‐head coordination (DHC) strategy is proposed. Leveraging a shared latent space and decoder backbone, the model simultaneously executes unsupervised data reconstruction and supervised quality prediction. This strategy effectively overcomes the label scarcity issue by explicitly mining the latent information embedded in unlabelled data. The model is optimized via a weighted objective function that integrates KL divergence, reconstruction and prediction loss. Simulation experiments on a debutanizer column and a thermal power generation process demonstrate that the proposed method outperforms baseline methods, including VAE, VAE‐LSTM, VRNN, and temporal convolutional networks (TCN)‐VAE, verifying its effectiveness and superiority in handling complex industrial data.