Decentralized diagnostics based on a distributed micro-genetic algorithm for transducer networks monitoring large experimental systems.

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ID: 171726
2014
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Abstract
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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arpaia2014decentralizedthe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Arpaia, P;Cimmino, P;Girone, M;La Commara, G;Maisto, D;Manna, C;Pezzetti, M;
Journal The Review of scientific instruments
Year 2014
DOI
10.1063/1.4894210
URL
Keywords
otorhinolaryngology collaboration physicians health information system online systems

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