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  • 标题:Fault detection for geological drilling processes using multivariate generalized Gaussian distribution and Kullback Leibler divergence ⁎
  • 本地全文:下载
  • 作者:Yupeng Li ; Weihua Cao ; Wenkai Hu
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2020
  • 卷号:53
  • 期号:2
  • 页码:164-169
  • DOI:10.1016/j.ifacol.2020.12.115
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThe presence of downhole faults compromises the safety and also leads to increased maintenance costs in complex geological drilling processes. In order to achieve timely and accurate detection of downhole faults, a systematic fault detection method is proposed based on the Multivariate Generalized Gaussian Distribution (MGGD) and the Kullback Leibler Divergence (KLD). Uncorrelated components are obtained from the original drilling process signals using the principle component analysis; then, the distribution of components is estimated using the MGGD; afterwards, the KLD is calculated based on a deduced analytic formula; last, the downhole faut is detected by comparing the calculated KLD with the alarm threshold obtained from normal data. The effectiveness and practicality of the proposed method are demonstrated by application to a real drilling process.
  • 关键词:KeywordsGeological drilling processesfault detectionKullback Leibler Divergencemultivariate generalized Gaussian distribution
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