  As the manufacturing industry advances with sensor integration and
automation, the opaque nature of deep learning models in machine learning poses
a significant challenge for fault detection and diagnosis. And despite the
related predictive insights Artificial Intelligence (AI) can deliver, advanced
machine learning engines often remain a black box. This paper reviews the
eXplainable AI (XAI) tools and techniques in this context. We explore various
XAI methodologies, focusing on their role in making AI decision-making
transparent, particularly in critical scenarios where humans are involved. We
also discuss current limitations and potential future research that aims to
balance explainability with model performance while improving trustworthiness
in the context of AI applications for critical industrial use cases.
