Enhanced process monitoring for industrial coking furnace using a dual-channel pooling and homologous bilinear model-based convolutional neural network

This paper introduces a process monitoring method using a convolutional neural network (CNN) with dual-channel pooling and homologous bilinear models (DCP-HBM). This method aims to enhance the stability of industrial coking furnaces and reduce maintenance costs. It is significantly relevant for addressing challenges such as complexity, high dimensionality, and strong coupling, which are difficult to manage with traditional fault diagnosis methods. In this study, a deep feature extraction module is employed to gather feature information. To effectively reduce the impact of noise and strengthen the correlation between features, DCP is incorporated after deep feature extraction. Concurrently, a HBM is introduced for feature fusion, further refining the characteristics of each state. Faults are then accurately classified through a classification module. The method utilizes DCP to filter and focus input data, thus making the model more attentive to task-relevant information. This enhances the model's understanding and representation of the input data. The introduction of the HBM further refines the features extracted by the network and increases the precision of feature extraction. This leads to improved recognition of faults with high similarity, thereby enhancing the accuracy of the overall process monitoring. Experimental results demonstrate that this method exhibits strong performance in the monitoring of industrial coking furnaces, indicating its wide-ranging application prospects.