Remaining useful life prediction of multi-sensor monitored degradation systems with health indicator
编号:69 访问权限:公开 更新:2022-12-22 13:45:01 浏览:377次 张贴报告

报告开始:暂无开始时间(Asia/Shanghai)

报告时间:暂无持续时间

所在会场:[暂无会议] [暂无会议段]

摘要
To evaluate degradation processes of rolling bears in real-time devices, health indicators (HIs) are required to be built. Due to the constraints of sensors, the degradation pattern cannot be denoted by commonly used signals such as vibration data. Moreover, the practical requirements of HI for prognostics are always ignored, such as monotonicity and trendability. Therefore, a novel HI construction method based on reinforcement learning (RL) is proposed.
关键词
Reinforcement learning; HI construction; Data fusion; RUL
报告人
Xucong Huang
Ms student Beihang University

Xucong Huang was born in Henan, China. He
received the B.S. degree from the School of Automation
Science and Electrical Engineering, Beihang
University, Beijing, China, in 2020, where he is
currently working toward the M.S. degree.
His research interests include equipment health
assessment and remaining useful life prediction.

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重要日期
  • 会议日期

    11月30日

    2022

    12月02日

    2022

  • 11月30日 2022

    初稿截稿日期

  • 12月24日 2022

    报告提交截止日期

  • 04月13日 2023

    注册截止日期

主办单位
Harbin Insititute of Technology
China Instrument and Control Society
Heilongjiang Instrument and Control Society
Chinese Institute of Electronics
IEEE I&M Society Harbin Chapter
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