A hybrid fault detection and diagnosis method in server rooms' cooling systems

Yulia Berezovskaya, Chen-Wei Yang, Arash Mousavi, Xiaojing Zhang, Valeriy Vyatkin

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019
PublisherIEEE
Pages1405-1410
Number of pages6
ISBN (Electronic)9781728129273
DOIs
Publication statusPublished - 1 Jul 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Industrial Informatics - Aalto University, Helsinki-Espoo, Finland
Duration: 22 Jul 201925 Jul 2019
Conference number: 17
https://www.indin2019.org/

Publication series

NameIEEE International Conference on Industrial Informatics
PublisherIEEE
ISSN (Print)1935-4576
ISSN (Electronic)2378-363X

Conference

ConferenceIEEE International Conference on Industrial Informatics
Abbreviated titleINDIN
CountryFinland
CityHelsinki-Espoo
Period22/07/201925/07/2019
Internet address

Keywords

  • Classification
  • Cooling system
  • Data center
  • Fault detection

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