This paper proposes a standards-aligned hybrid artificial intelligence–digital twin (DT) framework for predictive maintenance (PdM) in the maritime domain under conditions of data scarcity and heterogeneous sensor environments. The proposed framework adopts a DT-ready reference architecture centered on an ISO 19848-aligned data contract enabling consistent signal naming across vessels and equipment. On this foundation, the prognostics module is designed as a Domain-Knowledge Enhanced LSTM (DK-LSTM), a constraint-regularized sequence model in which three domain-informed constraints—(i) RUL non-negativity, (ii) monotonic degradation, and (iii) operating-range upper bounds—are formulated within the learning objective. Constraints (i) and (iii) are active throughout, while constraint (ii) is reserved for future work due to the structural limitation of batch-sort approximation in single-output architectures. An asymmetric safety penalty further suppresses hazardous over-predictions. Scenario-based virtual experiments are conducted using the NASA C-MAPSS turbofan degradation benchmark, evaluated under (1) sensor missingness via masking indicators and (2) structural domain shift comprising operational-condition shift (E3a: FD001 → FD002) and fault-mode shift (E3b: FD001 → FD003). Through systematic ablation of loss weights and stabilization techniques across multi-seed verification (seeds 0, 42, 123), the final stabilized configuration (DK-LSTM-v4) demonstrates robust safety-critical prediction in zero-shot domain-shift scenarios: 43.7% NASA Score improvement over the strongest baseline (GRU) under E3a and 20.8% improvement under E3b. The model trades modest in-domain performance for substantial cross-domain robustness, aligning with the core requirement of safety-critical maritime and defense applications where target-domain training data is unavailable.
Park et al. (2026) studied this question.