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Machine learning (ML) and Artificial Intelligence (AI) are increasingly usedin energy and engineering systems, but these models must be fair, unbiased, andexplainable. It is critical to have confidence in AI's trustworthiness. MLtechniques have been useful in predicting important parameters and improvingmodel performance. However, for these AI techniques to be useful for makingdecisions, 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 remaininguseful life (RUL) in a digital twin system to make it intelligent whileensuring that the AI model is transparent in its decision-making processes andthat the predictions it generates can be understood and trusted by users. Byusing AI that is explainable, interpretable, and trustworthy, intelligentdigital twin systems can make more accurate predictions of RUL, leading tobetter maintenance and repair planning and, ultimately, improved systemperformance. The objective of this paper is to understand the idea of XAI andIML and justify the important role of ML/AI in the Digital Twin framework andcomponents, which requires XAI to understand the prediction better. This paperexplains the importance of XAI and IML in both local and global aspects toensure the use of trustworthy ML/AI applications for RUL prediction. This paperused the RUL prediction for the XAI and IML studies and leveraged theintegrated python toolbox for interpretable machine learning (PiML).

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