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).

翻译：人工智能（AI）与机器学习（ML）正越来越多地应用于能源与工程系统中，但这些模型必须做到公平、无偏且可解释。对AI可信度的置信至关重要。机器学习技术已在预测关键参数和提升模型性能方面展现出实用性。然而，要使这些AI技术有效辅助决策，它们需具备可审计性、可追溯性及易理解性。因此，采用可解释人工智能（XAI）与可解读机器学习（IML）对于数字孪生系统中精准预测剩余寿命（RUL）等预测性指标至关重要——这既能实现系统的智能化，又能确保AI模型的决策过程透明，使生成的预测结果被用户理解与信赖。通过运用可解释、可解读且可信的AI，智能数字孪生系统能够更精准地预测RUL，从而优化维护与维修规划，最终提升系统性能。本文旨在阐释XAI与IML的核心概念，论证AI/ML在数字孪生框架与组件中的关键作用（即需借助XAI深化预测理解），并从局部与全局两个维度阐明XAI与IML对保障RUL预测中可信AI/ML应用的重要性。我们针对XAI与IML研究采用了RUL预测案例，并运用了集成式Python可解读机器学习工具箱（PiML）。