Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review
It tackles the problem of black-box AI models for engineers and operators in manufacturing, but it is incremental as it reviews existing methods rather than proposing new ones.
This paper reviews explainable AI (XAI) techniques to address the opacity of deep learning models in fault detection and diagnosis for manufacturing, aiming to improve transparency and trustworthiness in critical industrial applications.
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.