Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review

TL;DR

This review examines explainable AI methods in manufacturing fault detection, emphasizing their importance for transparency, trust, and improved decision-making in industrial applications.

Contribution

It provides a comprehensive overview of XAI techniques applied to fault detection, highlighting current limitations and future research directions for industrial AI systems.

Findings

XAI enhances transparency in fault diagnosis models.

Current XAI methods face trade-offs between explainability and accuracy.

Future research aims to improve trustworthiness and performance balance.

Abstract

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.