A transducer based on a virtual flow meter is proposed for monitoring helium distribution and consumption in cryogenic systems for particle accelerators. The virtual flow meter allows technical and economical constraints, preventing installation of physical instruments in all the needed measurement points, to be overcome. Virtual flow meter performance for the alternative models of Samson [ http://www.samson.de (2015)] and Sereg-Schlumberger [ http://www.slb.com/ (2015)] is compared with the standard IEC 60534-2-1 [Industrial-process control valves-Part 2-1: Flow capacity-sizing equations for fluid flow under installed conditions (2011), https://webstore.iec.ch/publication/2461], for a large temperature range, for both gaseous and liquid helium phases, and for different pressure drops. Then, the calibration function of the transducer is derived. Finally, the experimental validation for the helium gaseous state on the test station for superconducting magnets in the laboratory SM18 [Pirotte et al., AIP Conf. Proc. 1573, 187 (2014)] at CERN is reported.
A model-based method for fault detection and early-stage isolation, applicable when unfaulty conditions can be identified only by a reduced number of trials (even only one), is presented. The basic idea is to model analytically the uncertainty of the unfaulty frequency response and express the fault condition in terms of the noise power variance. A preliminary fault isolation is carried out by sensitivity analysis in order to identify the most influencing model parameters and assess their influence on the estimated noise. Then, during maintenance tests, the noise power is checked to detect the faulty condition. This technique is conceived to check the quality of a critical component in an experimental installation (fault detection and early-stage isolation), as well as to detect its faulty dynamic behaviors over a long horizon maintenance test campaign (condition monitoring). The method was applied to four cold compressors with active magnetic bearings at CERN by proving to be able to detect an actual faulty condition in one of such compressors.
Evolutionary approach to centralized multiple-faults diagnostics is extended to distributed transducer networks monitoring large experimental systems. Given a set of anomalies detected by the transducers, each instance of the multiple-fault problem is formulated as several parallel communicating sub-tasks running on different transducers, and thus solved one-by-one on spatially separated parallel processes. A micro-genetic algorithm merges evaluation time efficiency, arising from a small-size population distributed on parallel-synchronized processors, with the effectiveness of centralized evolutionary techniques due to optimal mix of exploitation and exploration. In this way, holistic view and effectiveness advantages of evolutionary global diagnostics are combined with reliability and efficiency benefits of distributed parallel architectures. The proposed approach was validated both (i) by simulation at CERN, on a case study of a cold box for enhancing the cryogeny diagnostics of the Large Hadron Collider, and (ii) by experiments, under the framework of the industrial research project MONDIEVOB (Building Remote Monitoring and Evolutionary Diagnostics), co-funded by EU and the company Del Bo srl, Napoli, Italy.
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