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  • 标题:Predicting anomaly conditions of energy equipment using neural networks
  • 本地全文:下载
  • 作者:Anastasia Sverdlova ; Artur Zaporozhets
  • 期刊名称:E3S Web of Conferences
  • 印刷版ISSN:2267-1242
  • 电子版ISSN:2267-1242
  • 出版年度:2021
  • 卷号:280
  • 页码:1-10
  • DOI:10.1051/e3sconf/202128009005
  • 语种:English
  • 出版社:EDP Sciences
  • 摘要:In modern conditions for complex thermal power facilities, the issue of developing methods for predicting equipment failures is especially relevant. Methods based on the intellectualization of diagnostic systems and allowing to obtain predictive models based on the use of both current data received in real time from measuring equipment and retrospective information are considered promising. Intellectualization of the system in terms of the ability to learn allows to quickly adjust the parameters of forecasting models under changing conditions of equipment operation, to determine new deadlines for scheduled repairs and minimize equipment downtime. A limitation of the use of methods is the incompleteness of failure statistics, ie when equipment failures are rare or non-existent. Such diagnostics of energy equipment, especially thermal power facilities, contributes to a more environmentally friendly production.
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