Statistical Digital Twins with Explainable AI: A Machine Learning Framework for Dynamic Prediction and Risk Intelligence

Abstract

Digital twins increasingly combine sensor streams, engineering models, simulation, and machine learning to support diagnosis, prognosis, optimization, and risk-sensitive operational decisions. Current standards and research, however, increasingly emphasize that a trustworthy digital twin must also provide credible uncertainty, lifecycle traceability, interoperability, and interpretable decision support. NIST identifies synchronized operation, model credibility, interoperability, and VVUQ as important requirements for advanced-manufacturing digital twins, while the ISO 23247 family supplies manufacturing-specific principles, digital-thread guidance, and composition mechanisms. [12] This paper proposes a Statistical Digital Twin with Explainable AI (ST-XAI) framework for dynamic prediction and risk intelligence. The architecture represents latent asset health probabilistically; integrates multivariate telemetry, operating context, asset metadata, event history, and optional physics/simulation outputs; applies causal multi-scale temporal feature engineering; supports classical, ensemble, sequence, Bayesian, and hybrid prognostic models; quantifies uncertainty through heteroscedastic estimation and conformal calibration; converts prognostic outputs into horizon-specific event probabilities; and integrates global, local, and temporal explanations using SHAP-family methods. The design is intended to make prediction, uncertainty, risk, explanation, and provenance parts of a single synchronized decision system rather than separate analytical products. A two-track experimental design is defined. NASA C-MAPSS is adopted as the proposed open-source external RUL benchmark; NASA describes the dataset as four turbofan degradation simulations under different combinations of operating conditions and fault modes with multiple sensor channels. [10] A second, fully controlled synthetic dataset is executed to permit known degradation dynamics, ablation experiments, calibration tests, and uncertainty stress analysis. The executed dataset contains 120 run-to-failure assets, 18,871 observations, and 79 causal predictors. Asset-level 60/20/20 train/validation/test splitting prevents trajectory leakage. On the synthetic locked test set, the validation-selected predictor achieves RMSE 5.842 cycles, MAE 4.039 cycles, and R² 0.9756. For failure within 30 cycles, calibrated risk prediction achieves AUROC 0.9971 and AUPRC 0.9861, while isotonic calibration lowers expected calibration error from 0.0192 to 0.00935. Removing rolling temporal features increases XGBoost RMSE by approximately 9.9%, demonstrating their value in the controlled experiment. Normalized split-conformal intervals nevertheless under-cover on unseen assets, with nominal 90% coverage yielding 84.0% empirical coverage. The latter finding illustrates why calibration and distribution-shift monitoring must be intrinsic capabilities of a statistical digital twin. The study concludes that a decision-grade digital twin should be regarded as a continuously updated statistical belief-and-decision system. Trustworthy operation requires explicit modeling of uncertainty, probability calibration, time-aware explanations, data/model lineage, distribution-shift detection, human oversight, and continuous VVUQ not point prediction alone.