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A multi-model fusion method based on 0-1 programming and its application in early warning of coal mill failure

  • Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In practical operation of the thermal power plant, fault on coal mill is one of the principal factors that hinder the power plant from completing the power generating tasks on schedule. Therefore, early warning on coal mill failures is of great significance to guarantee the stability of thermal power plant and reliability of power supply to the grid system. In this paper, a multi-model fusion method based on 0-1 programming for early warning of the coal mill failure is proposed and evaluated. For this method, the background information and main features of the mill failure problems are taken into consideration, and an optimization algorithm is implemented to combine the results of multiple models. In the optimization algorithm, the sum of the false alarm cost and false negative cost is the objective, and whether accept the pre-warning or not is the variable. The method is evaluated in practical situations, and numerical test shows that the total cost of false alarm and false negative of multi-model fusion method is more promising than that of each single model. The validity is proved through the result.

Original languageEnglish
Title of host publicationProceedings of the 38th Chinese Control Conference, CCC 2019
EditorsMinyue Fu, Jian Sun
PublisherIEEE Computer Society
Pages4860-4865
Number of pages6
ISBN (Electronic)9789881563972
DOIs
StatePublished - Jul 2019
Event38th Chinese Control Conference, CCC 2019 - Guangzhou, China
Duration: 27 Jul 201930 Jul 2019

Publication series

NameChinese Control Conference, CCC
Volume2019-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference38th Chinese Control Conference, CCC 2019
Country/TerritoryChina
CityGuangzhou
Period27/07/1930/07/19

Keywords

  • 0-1 programming
  • Coal mill
  • Fault early warning
  • Multi-model fusion

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