A Sentinel-Based Adaptive Hybrid Soft Sensing Method for Industrial Process Monitoring

In dynamic industrial environments, effective soft-sensing methods need adapt to real-time variations in process conditions. Though recent efforts have integrated deep learning and handcrafted features to design hybrid feature methods while reducing reliance on diverse training data, existing approaches often rely on fixed fusion strategies, limiting their adaptability. In this study, we propose a sentinel-based adaptive hybrid soft sensing (SAHSS) method, which dynamically shifts between deep learning and handcrafted features to enhance representation adaptability under changing conditions. The sensing model consists of a deep learning monitoring branch, a handcrafted monitoring branch, and a sentinel. The sentinel serves as a decision-making unit that dynamically selects between the deep learning and handcrafted branches based on image reconstruction error. To extract sensitive image features for the deep learning branch and evaluate their reliability for the sentinel, we develop a mixup bidirectional adversarial autoencoder (MixBiAAE). This autoencoder leverages feature interpolation to smooth and stabilize the learned latent space, thereby enhancing the invertible relationship between the image encoder and generator. Additionally, to help the hybrid sensing model adapt to fluctuating working conditions, a momentum memory network (MoMN) -based regression module is designed for both branches, allowing incremental updates with new data without retraining the model. Extensive experiments on an industrial flotation dataset demonstrate that our method outperforms existing hybrid soft sensing techniques, offering a robust and adaptive solution for industrial process monitoring.