Online analysis for anticipated failure diagnostics of the CERN cryogenic systems

The cryogenic system is one of the most critical component of the CERN Large Hadron Collider (LHC) and its associated experiments ATLAS and CMS. In the past years, the cryogenic team has improved the maintenance plan and the operation procedures and achieved a very high reliability. However, as the recovery time after failure remains the major issue for the cryogenic availability new developments must take place. A new online diagnostic tool is developed to identify and anticipate failures of cryogenics field equipment, based on the acquired knowledge on dynamic simulation for the cryogenic equipment and on previous data analytic studies. After having identified the most critical components, we will develop their associated models together with the signature of their failure modes. The proposed tools will detect deviation between the actual systems and their model or identify preliminary failure signatures. This information will allow the operation team to take early mitigating actions before the failure occurrence. This contribution will present the overall architecture of the proposed tool, the methods used to identify critical components, the characteristic failure model to recognize together with the implementation plan and the achieved results.