Sensor Data Prediction in Process Industry by Capturing Mixed Length of Time Dependencies

研究成果: Conference contribution

抄録

Sensor Data prediction has been an interesting and practical topic in many domains. In the process industry, sensor data prediction can help us detect, diagnose and even predict possible failures to reduce unnecessary losses. Due to the complex relationship among multiple sensors, it is challenging to accurately predict the time series of multivariate sensors. In this research, we aim to solve the problem of predicting the time series of several related sensor data and proposed a novel structure for addressing with this provocative problem. More specifically, several proposed mixed length dilation layers and recurrent cells are used to capture mixed length of time dependencies. Experiments demonstrate that our proposed model indicates competitiveness in predicting comparing with other baseline methods.

本文言語English
ホスト出版物のタイトル2021 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1174-1178
ページ数5
ISBN(電子版)9781665437714
DOI
出版ステータスPublished - 2021
イベント2021 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021 - Virtual, Online, Singapore
継続期間: 2021 12月 132021 12月 16

出版物シリーズ

名前2021 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021

Conference

Conference2021 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021
国/地域Singapore
CityVirtual, Online
Period21/12/1321/12/16

ASJC Scopus subject areas

  • 戦略と経営
  • 人工知能
  • コンピュータ サイエンスの応用
  • 情報システムおよび情報管理
  • 産業および生産工学
  • 制御と最適化

フィンガープリント

「Sensor Data Prediction in Process Industry by Capturing Mixed Length of Time Dependencies」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル