DOI: 10.11648/j.acis.20261403.12 ISSN: 2328-5591

An Uncertainty-Calibrated Ensemble Framework for Fault Diagnosis in Industrial Hydraulic Power Units: Rethinking Stacked Neural Architectures

Emmanuel Enejor, Nelson Ibigbami, Ismail Lawal, Temidayo Adeoti, Ademola Adesoye, Samson Ajibesin, Adekunle Adeniyi, Kolawole Jayeola, Oludare Shotade
Show PDF Cite
Stacked artificial neural network architectures have been proposed for industrial condition monitoring on the premise that adding sequential learning stages improves predictive accuracy, yet this premise is rarely tested against simpler alternatives under leakage-free, cross-validated evaluation. This study conducts a rigorous, block-aware empirical investigation of predictive fault diagnosis on the benchmark UCI Condition Monitoring of Hydraulic Systems dataset (2,205 operating cycles, 17 multi-rate sensors, four superimposed component-degradation labels). We first demonstrate that naive random cross-validation on this dataset inflates reported accuracy by up to 26 percentage points relative to a block-aware (leakage-free) protocol, because the dataset is organised as long contiguous condition blocks. Under the corrected protocol, we reproduce and stress-test the stacking mechanism used by a representative elaborative stepwise stacked neural network (ESSANN) architecture from the recent literature, implementing it faithfully as a deep-ensemble multilayer perceptron (MLP) with feature-concatenation stacking and a boosting-style hard-example variant, and evaluate all architectures under 4-fold block-aware cross-validation with paired per-fold robustness checks. Both neural stacking variants degrade accuracy relative to a single-stage MLP ensemble consistently across every fold on both primary targets (valve condition, accumulator pressure), revealing that naive depth is not a principled correction mechanism and is vulnerable to training-data starvation. In contrast, a two-stage Random Forest architecture and Gradient Boosting, which stage learners on the complete training set every round, improve on the single-stage baseline consistently across all folds on valve condition (Gradient Boosting: 98.1% ± 1.1%, +11.3 points over Random Forest in every fold), but the same improvement is not sign-consistent across folds for accumulator pressure, where no tested architecture reliably outperforms a plain Random Forest baseline once fold-to-fold variance is accounted for. We further show that predictive entropy from a deep ensemble is a well-calibrated, monotonic indicator of classification risk (100% accuracy in the lowest-entropy quintile versus 41-63% in the highest), but that thresholds calibrated on training-set entropy are unusable in deployment, over-flagging 94-98% of unseen samples as "uncertain"; calibrating instead on a held-out validation block reduces this to a deployable 44-60%. We conclude with a practical, evidence-graded architecture recommendation and a risk-stratified decision framework for confidence-gated industrial control actions.

More from our Archive