Speaker
Description
Artificial Intelligence (AI) has shown become very popular as modelling strategy within statistical process monitoring (SPM), particularly in detecting abnormal process behaviours. However, for existing AI-based SPM methods, diagnosing features associated with signal remains challenging, as traditional diagnosis methods are not directly applicable. This lack of diagnosis makes it difficult to make an out-of-control action plan and take appropriate actions once a signal is detected, and thus impedes the AI-based SPM methods from being applied in practice. Explainable AI (XAI) offers a promising framework for addressing this limitation by providing feature relevance information for the model outputs, which can help identify the features related to abnormal process behaviour. This work proposes a general framework for combining XAI with AI-based monitoring. Simulation studies and a real-world case study show the effectiveness of the proposed method.
| Classification | Mainly methodology |
|---|---|
| Keywords | Signal diagnosis, XAI, statistical process monitoring |