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Subspace Clustering Based Association Analysis Between Multiple Process-Variable-Parameters and Faults

  • Yuyang Zhong
  • Ke Zhang
  • Yi Chai
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)

Abstract

Aiming at the problem of large amount of data and low utilization rate in complex industrial systems and processes, an association analysis method of process variables and faults is proposed. Because of the characteristic that large number of process variables and large data volume consist in complex industrial system, a subspace clustering based quantitative association rule mining method is proposed to the association analysis between multiple process-variables and faults. The validity and efficiency of the method is verified by using the fault datasets of TE process.

Keywords

Multiple process-variable-parameter Association analysis Quantitative association rules Subspace clustering Data mining Fault diagnosis 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of AutomationChongqing UniversityChongqingChina

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