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

Transparency is critical in high-stakes industrial settings: The paper emphasizes that deep learning models used for fault detection in manufacturing often operate as "black boxes," making it difficult for human operators to understand why the AI made a particular diagnosis. This lack of transparency is especially problematic in critical scenarios where equipment failures could have serious consequences, highlighting why explainable AI (XAI) tools are essential for responsible deployment.

XAI techniques can bridge the trust gap between humans and AI systems: By making AI decision-making processes visible and understandable, XAI methodologies help build trustworthiness in industrial applications. The paper suggests that when operators can see how an AI system reached its conclusions about equipment faults, they are more likely to rely on and appropriately use these AI tools in real-world manufacturing environments.

There's an ongoing tension between accuracy and explainability: The paper identifies a key challenge in responsible AI implementation: balancing model performance with the ability to explain decisions. This suggests that organizations cannot simply adopt the most powerful AI models without considering whether they can be made interpretable—a trade-off that requires careful consideration in critical industrial contexts.

Current XAI tools need further development for practical use: The review indicates that while various XAI methodologies exist, there are still significant limitations in how well they work in real manufacturing scenarios, suggesting that the field is still evolving and organizations should be cautious about assuming current techniques fully solve the explainability problem.