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Found 3 papers, 2 papers with code
Fault Identification Enhancement with Reinforcement Learning (FIERL)
This letter presents a novel approach in the field of Active Fault Detection (AFD), by explicitly separating the task into two parts: Passive Fault Detection (PFD) and control input design.
Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD
While Machine Learning has become crucial for Industry 4. 0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challenge exacerbated in the evolving Industry 5. 0 with its human-centric focus.
AcME-AD: Accelerated Model Explanations for Anomaly Detection
Pursuing fast and robust interpretability in Anomaly Detection is crucial, especially due to its significance in practical applications.
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