Predictive maintenance is a well studied collection of techniques that aims to prolong the life
of a mechanical system by using artificial intelligence and machine learning to predict the …

Artificial intelligence (AI) and Machine learning (ML) are increasingly used for digital twin
development in energy and engineering systems, but these models must be fair, unbiased …

This paper presents a comprehensive survey on interpretable intelligent fault diagnosis for
rotating machinery, addressing the challenge of the “black box” nature of machine learning …

Abstract Machine learning (ML)/deep learning (DL) has shown tremendous success in data-
driven predictive maintenance (PdM). However, operators and technicians often require …

To understand the complex nature of the Artificial Intelligence (AI) model, the model needs to
be more trustable, transparent, scalable, understandable, and explainable. The trust of the …

Artificial intelligence and cyber-physical systems (CPS) are two of the key technologies of
the future that are enabling major global shifts. However, most of the current …

Deep learning approaches have recently been extensively explored for the prognostics of
industrial assets. However, they still suffer from a lack of interpretability, which hinders their …

The world is relying more and more on renewable energy sources to cater the global energy
demand. Consequently, the renewable energy systems are becoming more and more …

Abstract The prediction of Remaining Useful Life (RUL) in aerospace engines is a challenge
due to the complexity of these systems and the often-opaque nature of machine learning …

Artificial Intelligence and Machine Learning are the latest topics across industries. A lot of
concentration has been given to these areas and still the adoption has been challenged by …