Novel Deep Learning Framework for Evaporator Tube Leakage Estimation in Supercharged Boiler

The estimation of leakage faults in evaporation tubes of supercharged boilers is crucial for ensuring the safe and stable operation of the central steam system. However, leakage faults of evaporation tubes feature high time dependency, strong coupling among monitoring parameters, and interference from noise. Additionally, the large number of monitoring parameters (approximately 140) poses a challenge for spatiotemporal feature extraction, feature decoupling, and establishing a mapping relationship between high-dimensional monitoring parameters and leakage, rendering the precise quantitative estimation of evaporation tube leakage extremely difficult. To address these issues, this study proposes a novel deep learning framework (LSTM-CNN–attention), combining a Long Short-Term Memory (LSTM) network with a dual-pathway spatial feature extraction structure (ACNN) that includes an attention mechanism(attention) and a 1D convolutional neural network (1D-CNN) parallel pathway. This framework processes temporal embeddings (LSTM-generated) via a dual-branch ACNN—where the 1D-CNN captures local spatial features and the attention models’ global significance—yielding decoupled representations that prevent cross-modal interference. This architecture is implemented in a simulated supercharged boiler, validated with datasets encompassing three operational conditions and 15 statuses in the supercharged boiler. The framework achieves an average diagnostic accuracy (ADA) of over 99%, an average estimation accuracy (AEA) exceeding 90%, and a maximum relative estimation error (MREE) of less than 20%. Even with a signal-to-noise ratio (SNR) of −4 dB, the ADA remains above 90%, while the AEA stays over 80%. This framework establishes a strong correlation between leakage and multifaceted characteristic parameters, moving beyond traditional threshold-based diagnostics to enable the early quantitative assessment of evaporator tube leakage.