New Attention Ensemble-Based Fault Diagnosis for Industrial Processes Under Imbalanced Finite Data

During industrial processes operation, fault diagnosis needs to rely on finite and imbalanced samples for learning because the equipment is usually in a normal operation state and the probability of faults is low. For this issue, we propose a new attention ensemble algorithm, DMSAM-OAdB. First, the multiscale sparse attention module (MSAM) extracts and fuses feature information from different scales to establish the correlation among features and adaptively adjusts the feature weights by soft thresholding operation to emphasize the critical features while filtering the redundant information. Subsequently, a deep network model (DMSAM) constructed by stacking multiple convolutional layers and MSAMs is iteratively trained as the base classifier for the optimized AdaBoost algorithm (OAdB). The OAdB dynamically increases the weights of misclassified samples while mitigating class imbalance by randomly zeroing out the weights of a certain number of correctly classified majority class samples. Finally, the improved focal loss (IFL) function enhances the model’s discrimination capability for minority class samples. Experiments on the industrial coking furnace validate the reliability and superiority of the proposed methodology.