Broad learning system combined with inter‐layer mutual attention‐based stacked performance‐relevant autoencoder for industrial process operating performance assessment

Process operating performance assessment (POPA) enables enterprises to comprehensively monitor production process states in real time, ensuring optimal operational conditions. Traditional POPA methods either lack real‐time analysis capabilities due to post‐event assessment or struggle to distinguish performance grades in process data with subtle differences due to shallow learning architectures. To address these limitations, this study proposes a broad learning system combined with inter‐layer mutual attention‐based stacked performance‐relevant autoencoder (IMASPAE‐BLS) for online POPA applications. First, to extract highly discriminative deep features from high‐dimensional data with weak inter‐grade differences, IMASPAE is employed to perform multi‐layer feature extraction on raw modelling data. By introducing performance grade labels into the stacked autoencoder framework and integrating an inter‐layer mutual attention mechanism, the model effectively fuses feature information across different hidden layers. This design not only mitigates the limitations of traditional BLS in deep feature extraction due to its flat structure but also fully captures performance‐related features from various layers, thereby reducing information loss and enhancing classification capability. Second, two distinct enhancement node generation and cascading strategies are designed to improve the adaptability of the model to various data types and to strengthen its data mining capacity. Furthermore, an adaptive algorithm is proposed to determine the optimal number of enhancement nodes, ensuring high model performance while addressing the uncertainty in node quantity in BLS. Finally, three industrial case studies demonstrate the effectiveness of the proposed model, showing its superiority over existing methods.