Remaining useful life prediction of multi-sensor monitored degradation systems with health indicator
ID:69 View Protection:PUBLIC Updated Time:2022-12-22 13:45:01 Hits:167 Poster Presentation

Start Time:Pending(Asia/Shanghai)


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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.
Reinforcement learning; HI construction; Data fusion; RUL
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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Important Date
  • Conference Date

    Nov 30



    Dec 02


  • Nov 30 2022

    Draft paper submission deadline

  • Dec 24 2022

    Contribution Submission Deadline

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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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