Hierarchical Balanced Pseudo-Label Random Forest for Imbalanced Industrial Process Fault Classification Under Limited Labels

Objective: This study addresses multiclass industrial fault classification when human labels are simultaneously scarce and class-imbalanced. The proposed hierarchical balanced pseudo-label random forest (HBPL-RF) is novel in coordinating class-adaptive confidence cut-points and per-class pseudo-label quotas with class-balanced forests, validation gating and rollback, soft fusion under extreme label scarcity, and a rare-fault-first decision branch. HBPL-RF was evaluated on the Cranfield multiphase-flow and Tennessee Eastman benchmarks at five labeled fractions and five random seeds. It produced the highest observed mean Macro F1 under the unified protocol: 0.7324 on Cranfield and 0.6109 on Tennessee Eastman. At the nominal 1% Cranfield level, the primary cross-validation-feasible allocation contains five labeled Case 6 windows per seed to support five-fold validation and yields Case 6 F1 = 0.6627 ± 0.0458. Under strict proportional sampling with three Case 6 labels, F1 falls to 0.4019 ± 0.0718; the primary value therefore must not be interpreted as ordinary proportional 1% random labeling. Capacity-matched controls, ablations, sensitivity analysis, and error decomposition are consistent with the intended class-control and rare-fault-protection mechanisms, but do not establish causal effects or universal superiority. The conclusions are restricted to the two benchmarks, declared labeling protocols, and experimental conditions studied.