TBDDQN: Imbalanced Fault Diagnosis for Blast Furnace Ironmaking Process via Transformer–BiLSTM Double Deep Q-Networks

The blast furnace ironmaking process (BFIP) is a highly complex and dynamic industrial system where strong spatiotemporal coupling and severe data imbalance pose substantial challenges for fault diagnosis. To address these issues, this study proposes a Transformer–BiLSTM Double Deep Q-Network (TBDDQN) framework for intelligent fault diagnosis. The framework employs a dual-branch architecture that integrates a Transformer-based spatial encoder with a BiLSTM-attention temporal extractor to capture global dependencies and dynamic patterns from multivariate time-series data. To mitigate class imbalance and asymmetric fault costs, a cost-sensitive reinforcement learning scheme based on Double DQN is incorporated, featuring prioritized experience replay and adaptive misclassification penalties. Experiments on real blast furnace datasets show that TBDDQN achieves a macro-averaged precision of 0.970 and a macro-averaged F1-score of 0.929, outperforming conventional CNN, LSTM, and DQN-based baselines. These results demonstrate that TBDDQN offers a robust and interpretable solution for imbalanced industrial fault diagnosis in the BFIP.