Machine Learning for Accelerator Operations Using Big Data Analytics / L-CAPE
Description
Big data analytics for anomaly prediction and classification, enabling automatic mitigation, operational savings, and predictive maintenance of the Fermilab LINAC
Detailed example
The ML outputs to a dashboard withfault labels and downtime predictiojns.The model will also try and predict dwwontime and possible actions.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
ML models are deployed for the FNAL's Linac.to detect, label andact upon faults. The usage of ML will jimporve our fault labelingand detection. This will allow for improved operatioal efficeincy, fault statistics, and preventitive maintenance. To my knowledge this is the first global accelerator operations ML system.
Audit / financial statement impact
The use case does not have an effect on civil rights/liberties/privacy, access to education/housing/insurance/credit/employment, access to critical government resources/services, human health/safety, critical infrastructure/public safety
Controls / human review
ATO: No; PIA: Not published
Data needed
my own simulated data; research datasets from scientific experiments