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Mining Association Rules from Multidimensional Transformer Defect Records

  • Yi YangEmail author
  • Yujie Geng
  • Yi Ju
  • Xuan Zhao
  • Danfeng Yan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 849)

Abstract

There are various types of transformer device defects and the formation reasons are complex. Exploring the influencing factors and occurrence of transformer devices defects is a focus in the field of power transmission and transformation devices state inspection and evaluation. This paper proposes an analysis method, multidimensional FP-Growth algorithm (MDFPG) to mine association rules from multidimensional transformer defect records. The method combines records from different system of power grid to construct multidimensional records first. Then, the records are preprocessed and encoded into single dimension form. The MDFPG method speeds up the mining process by adding a pruning step. Experiments show that MDFPG method has a better performance than FP-Growth algorithm on large data sets. Some conclusions can be learned from the experiment result, which has a certain value for making equipment maintenance plans and exploring defect occurrence regularity.

Keywords

Transformer Association rule mining FP-Growth 

Notes

Acknowledgments

This paper is supported by “National 863 project (No. 2015AA050204)” and “State Grid science and technology project (No. 520626170011)”.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Yi Yang
    • 1
    Email author
  • Yujie Geng
    • 1
  • Yi Ju
    • 2
  • Xuan Zhao
    • 3
  • Danfeng Yan
    • 3
  1. 1.State Grid ShanDong Electric Power Research InstituteJinanChina
  2. 2.State Grid Jinan Power Supply CompanyJinanChina
  3. 3.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina

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