AI-Driven Human-Centric Decision Support for Resilient Predictive Maintenance in Asset Management Amidst Pandemics

Abstract

Pandemic-induced disruptions such as those experienced during COVID-19 have exposed critical vulnerabilities in conventional predictive maintenance (PdM) workflows, particularly within complex industrial asset management environments. Maintenance scheduling relies on technician availability, spare-part logistics, and sensor fidelity—all of which deteriorate sharply under large-scale health crises. This paper introduces an AI-Driven Human-Centric Decision Support System (AI-HC-DSS) that integrates a Transformer-LSTM hybrid deep learning architecture with Shapley Additive Explanations (SHAP)-based explainability and a Bayesian uncertainty quantifier to deliver transparent, actionable maintenance recommendations under pandemic-era constraints. The system embeds two novel evaluation metrics—the Pandemic Resilience Score (PRS) and the Mean Time-to-Failure Degradation Index (MTTFDI)—to capture operational continuity and failure-progression robustness across disrupted scenarios. Experiments conducted on the NASA C-MAPSS turbofan degradation dataset and the MIMII industrial sound anomaly dataset demonstrate that the proposed framework outperforms competing baselines, achieving an F1-Score of 0.931, AUC-ROC of 0.924, RMSE of 9.87 cycles, and a PRS of 0.930, representing improvements of 6.5%, 6.3%, and 47.4% over the next-best Transformer-only baseline, respectively. The results confirm that coupling AI predictive power with human-centric design and pandemic-aware modeling yields substantially more resilient maintenance operations.