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:453 Poster Presentation

Start Time:Pending (Asia/Shanghai)

Duration:Pending

Session:[No Session] » [No Session Block]

Abstract
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.
Keywords
Reinforcement learning; HI construction; Data fusion; RUL
Speaker
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 Dates

15th August 2022 25th September 2022 - Manuscript Submission
15th October 2022 - Acceptance Notification
1st November 2022 - Camera Ready Submission
1st November 2022 10th November 2022Early Bird Registration

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