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Predictive Model for Calculating Abnormal Functioning Power Equipment

  • Alexandra A. KorshikovaEmail author
  • Alexander G. Trofimov
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 260)

Abstract

A method of early detection of defects in technological equipment of energy facilities is proposed. A brief analysis of the Russian market of cyber-physical industrial equipment monitoring systems was carried out. Special attention is paid to the problems of preparing initial data for training a model, in particular, the problem of obtaining adequate data on accidents that have occurred. A mathematical problem is formulated for modeling the anomaly index, which takes values from 0 (normal operation) to 1 (high probability of an accident). The model is based on well-known statistical methods. A method for dividing the periods of operation of technological equipment into “normal” and “anomalous” is proposed. The method of binary classification AUC ROC allows you to limit the number of signs involved in the formation of the anomaly indicator, signs that have a good “separation” ability. Using the Spearman’s rank correlation criterion, signs are selected that are most sensitive to the development of process equipment malfunctions. As an anomalous indicator, it is proposed to consider the ratio of the densities of distribution of the final signs, estimated in the anomalous and normal areas of operation of the process equipment. A method is proposed for generating an alarm for detecting the anomalous operation of the technological equipment of power units. It is shown that the proposed model made it possible to identify the beginning of the development of an emergency, while individual measurements did not detect any features of the operation of equipment of energy facilities in the pre-emergency time interval.

Keywords

Technological equipment Cyber-physical systems Defect detection Predictive analytics Linear regression AUC ROC 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alexandra A. Korshikova
    • 1
    Email author
  • Alexander G. Trofimov
    • 2
  1. 1.OOO InkontrolMoscowRussia
  2. 2.National Research Nuclear University “MEPhI”MoscowRussia

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