# Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life

arXiv: https://arxiv.org/abs/2301.06676 · HTML: https://mlsanity.com/paper/2301.06676/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 72 references that are in the map.
- First posted on arXiv 17 Jan 2023.

## Builds on (most cited first, 18 of 18)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators](https://mlsanity.com/paper/1910.03193/) (2019)
- [Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets](https://mlsanity.com/paper/2103.10974/) (2021)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions](https://mlsanity.com/paper/2003.07132/) (2020)
- [Explainable Neural Networks based on Additive Index Models](https://mlsanity.com/paper/1806.01933/) (2018)
- [Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification](https://mlsanity.com/paper/2011.04041/) (2020)
- [Designing Inherently Interpretable Machine Learning Models](https://mlsanity.com/paper/2111.01743/) (2021)
- [Explainable AI for Interpretable Credit Scoring](https://mlsanity.com/paper/2012.03749/) (2020)
- [Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems](https://mlsanity.com/paper/2109.00538/) (2021)
- [Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System](https://mlsanity.com/paper/2210.00074/) (2022)
- [An Explainable Regression Framework for Predicting Remaining Useful Life of Machines](https://mlsanity.com/paper/2204.13574/) (2022)
- [Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system](https://mlsanity.com/paper/2103.15636/) (2021)
- [The Need for Standardized Explainability](https://mlsanity.com/paper/2010.11273/) (2020)
- [Uncertainty Quantification and Sensitivity analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code](https://mlsanity.com/paper/2210.07541/) (2022)
- [Data-driven multi-scale modeling and robust optimization of composite structure with uncertainty quantification](https://mlsanity.com/paper/2210.09055/) (2022)

## Built on by (most cited first, 19 of 19)

- [Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutions](https://mlsanity.com/paper/2308.07523/) (2023)
- [Virtual Sensing-Enabled Digital Twin Framework for Real-Time Monitoring of Nuclear Systems Leveraging Deep Neural Operators](https://mlsanity.com/paper/2410.13762/) (2024)
- [Interpretable Prognostics with Concept Bottleneck Models](https://mlsanity.com/paper/2405.17575/) (2024)
- [Real-Time Sensing of Inaccessible Physical Fields via an Edge-Deployable Hardware-Portable Graph Neural Operator](https://mlsanity.com/paper/2604.01802/) (2026)
- [A Survey on Privacy Attacks Against Digital Twin Systems in AI-Robotics](https://mlsanity.com/paper/2406.18812/) (2024)
- [Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models](https://mlsanity.com/paper/2504.16969/) (2025)
- [Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators](https://mlsanity.com/paper/2507.11574/) (2025)
- [Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control](https://mlsanity.com/paper/2512.23292/) (2025)
- [ANSR-DT: A Neuro-Symbolic Framework for Adaptive and Explainable Digital Twins](https://mlsanity.com/paper/2501.08561/) (2025)
- [Single vs. Multiple Branches in DeepONet and S-DeepONet: Network Architecture Follows Coupling in Multiphysics Systems](https://mlsanity.com/paper/2507.03660/) (2025)
- [Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review](https://mlsanity.com/paper/2404.11597/) (2024)
- [Exploiting XAI maps to improve MS lesion segmentation and detection in MRI](https://mlsanity.com/paper/2409.03772/) (2024)
- [Continuous Monitoring of Large-Scale Generative AI via Deterministic Knowledge Graph Structures](https://mlsanity.com/paper/2509.03857/) (2025)
- [Techno-economic optimization of a heat-pipe microreactor, part I: theory and cost optimization](https://mlsanity.com/paper/2512.16032/) (2025)
- [Instance-level quantitative saliency in multiple sclerosis lesion segmentation](https://mlsanity.com/paper/2406.09335/) (2024)
- [The Role and Applications of Airport Digital Twin in Cyberattack Protection during the Generative AI Era](https://mlsanity.com/paper/2408.05248/) (2024)
- [Fuzzy expert system for the process of collecting and purifying acidic water: a digital twin approach](https://mlsanity.com/paper/2601.19527/) (2026)
- [Multi-Agent System-driven Digital Twins for predictive maintenance: architectures, technologies and open research challenges](https://mlsanity.com/paper/2607.21873/) (2026)
- [AgenticTwin: An Agentic LLM Framework Integrated with Digital Twin for Anomaly Detection](https://mlsanity.com/paper/2608.11679/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) and Machine learning (ML) are increasingly used in energy and engineering systems, but these models must be fair, unbiased, and explainable. It is critical to have confidence in AI's trustworthiness. ML techniques have been useful in predicting important parameters and in improving model performance. However, for these AI techniques to be useful for making decisions, they need to be audited, accounted for, and easy to understand. Therefore, the use of explainable AI (XAI) and interpretable machine learning (IML) is crucial for the accurate prediction of prognostics, such as remaining useful life (RUL), in a digital twin system, to make it intelligent while ensuring that the AI model is transparent in its decision-making processes and that the predictions it generates can be understood and trusted by users. By using AI that is explainable, interpretable, and trustworthy, intelligent digital twin systems can make more accurate predictions of RUL, leading to better maintenance and repair planning, and ultimately, improved system performance. The objective of this paper is to explain the ideas of XAI and IML and to justify the important role of AI/ML in the digital twin framework and components, which requires XAI to understand the prediction better. This paper explains the importance of XAI and IML in both local and global aspects to ensure the use of trustworthy AI/ML applications for RUL prediction. We used the RUL prediction for the XAI and IML studies and leveraged the integrated Python toolbox for interpretable machine learning~(PiML).