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IIR document

Chiller fault diagnosis with the technology of imbalanced data.

Number: pap. n. 1057

Author(s) : FAN Y., HAN H., CUI X., et al.

Summary

Data driven diagnostic model for refrigeration systems is often used exclusively to a dedicated object. When it comes to a different type of chiller, a new model must be trained with large among of normal and faulty data, which is both time-consuming and heavily data-depending, and accordingly, curbs its application. In this study, the technology for the tackling of imbalanced data was introduced to probe the possibility of extrapolating an old model trained for a centrifugal chiller to a new one that can diagnose the faults of a screw chiller, by just using small amount of new data. Synthetic Minority Oversampling Technique (SMOTE) is used to oversample the fault sample set with an unbalance ratio of 5% and support vector machine (SVM) is employed for fault diagnosis. By investigating oversampling ratios between 100% and 400%, it was found that the ratio of 100% was the best and the diagnostic accuracy reached 96.70% for the four types of faults of the screw chiller.

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Pages: 8

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Details

  • Original title: Chiller fault diagnosis with the technology of imbalanced data.
  • Record ID : 30026692
  • Languages: English
  • Source: Proceedings of the 25th IIR International Congress of Refrigeration: Montréal , Canada, August 24-30, 2019.
  • Publication date: 2019/08/24
  • DOI: http://dx.doi.org/10.18462/iir.icr.2019.1057
  • Notes:

    Keynote


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