Diagnosis Algorithm

Diagnosis algorithms aim to automatically identify the root cause of system failures or health issues, moving beyond simple anomaly detection. Current research emphasizes integrating anomaly detection with diagnosis, exploring diverse approaches like constraint-based methods (e.g., FastDiag and its parallel extensions), deep learning models for complex systems (e.g., Cyber-Physical Systems), and product-centric algorithms for specialized machinery (e.g., rotary indexing machines). A significant focus is on mitigating bias in healthcare algorithms, particularly addressing racial disparities in diagnosis, highlighting the need for community engagement and consideration of societal factors influencing data quality. These advancements hold promise for improving efficiency in various fields, from manufacturing to healthcare, by enabling faster, more accurate, and equitable diagnoses.