Bidirectional gated feature pyramid network‐decoupled variational autoencoder for robust soft sensing in multi‐rate industrial processes

Multi‐rate data are common in industrial processes and pose significant challenges for robust soft sensing. Although the multi‐rate pyramid variational autoencoder regression model has been developed to handle such data, it still suffers from latent variable feature entanglement and noise‐sensitive information propagation. To address these issues, this paper proposes a bidirectional gated feature pyramid network‐decoupled variational autoencoder for robust soft sensing in multi‐rate industrial processes. The proposed model decouples process data into quality‐relevant and quality‐irrelevant latent variables across multiple time scales. A mutual information maximization term is introduced to preserve key quality‐related information, thereby mitigating feature entanglement. In addition, a bidirectional gated fusion pyramid network is designed, which integrates top‐down semantic and bottom‐up detail streams and employs learnable gates to enable adaptive cross‐resolution fusion and effective noise suppression. Experimental results on numerical and industrial benchmark datasets demonstrate that the proposed method achieves superior prediction accuracy and robustness over mainstream approaches, offering an effective solution for multi‐rate soft sensing applications.