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A hybrid fault detection and diagnosis method in server rooms’ cooling systems
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0001-8185-7118
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0003-0075-1608
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0002-0799-2888
Vise andre og tillknytning
2019 (engelsk)Konferansepaper, Oral presentation with published abstract (Fagfellevurdert)
Abstract [en]

Data centers as all complex systems are prone to faults, and cost of them can be very high. This paper is focused on detecting the faults in the cooling systems, in particular on local fans level. In the paper, a hybrid approach is proposed. In the approach a model is used as substitute of the real system to generate dataset containing records of both normal and fault cases. On the generated data, machine learning algorithm or ensemble of algorithms are selected and trained to detect the faults. To demonstrate the approach, the rack model of real data center is created, and reliability of the model is shown. Using the model, the dataset with normal as well as abnormal records of data is generated. To detect faults of local fans, simple classifiers are built for all pairs: a local fan – a processor unit. Classifiers are trained on one part of generated data (training data), and then their accuracy is estimated on another part of generated data (test data). A real-time fault detection system is built based on the classifiers. The rack model is used as the substitute of the real plant to check operability of the system.

sted, utgiver, år, opplag, sider
2019.
HSV kategori
Identifikatorer
URN: urn:nbn:se:ltu:diva-75445OAI: oai:DiVA.org:ltu-75445DiVA, id: diva2:1341389
Konferanse
2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019
Tilgjengelig fra: 2019-08-08 Laget: 2019-08-08 Sist oppdatert: 2019-08-08

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Berezovskaya, YuliaYang, Chen-WeiMousavi, ArashZhang, XiaojingVyatkin, Valeriy

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