Prediction of Corrosion and Related Damage States in High-Temperature Equipment Using Mechanism and Data Fusion

High-temperature equipment in the petrochemical industry can develop multiple forms of damage during long-term service, with corrosion often accounting for a relatively high proportion. The presence of other damage forms complicates corrosion identification and equipment condition assessment. Accurate discrimination among damage types, followed by damage-severity prediction, is important for corrosion-risk screening and identifying equipment requiring priority inspection. Engineering inspection data commonly exhibit class imbalance, while damage severity has an inherent ordinal structure. These characteristics pose challenges to conventional classification models. Based on 660 operating and inspection records from high-temperature equipment, a mechanism–data fusion framework was developed for damage-type classification and damage-severity prediction. Mechanism-informed features describing damage evolution and data-driven risk encodings derived from the inspection records were used as model inputs. These two sources of information were further combined to form a comprehensive damage-driving index. Class imbalance was addressed through partial random over-sampling within each training fold to reduce the influence of uneven sample distributions. For damage-severity prediction, a cumulative ordinal neural network was employed together with a mechanism-consistency constraint guided by a label-independent physical trend index. Model performance was evaluated using stratified five-fold cross-validation, and ablation experiments were conducted to examine the contribution of individual modules. Benchmark comparisons and sensitivity analyses further assessed model performance and stability. With all components included, the mean accuracies for damage-type classification and damage-severity prediction reached 92.58% and 93.94%, respectively. Under the same data splits, both task-specific models outperformed their corresponding baseline methods, and the damage-severity model also performed better than the dedicated ordinal classifier. These findings indicate that the proposed framework can maintain reliable corrosion-identification performance across records containing multiple damage types and provide quantitative support for corrosion-risk screening and subsequent engineering assessment of high-temperature equipment.