Remaining Useful Life Prediction Based on Transformer with A Tiny Representation Network
ID:62 View Protection:PUBLIC Updated Time:2022-12-23 11:53:05 Hits:362 Poster Presentation

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Remaining useful life (RUL) prediction is of great significance to the prognostic and health management of rolling bearings. The effectiveness of the typical RUL prediction relies on the constructed health indicator (HI) which only represents limited degradation information. In addition, rolling bearing degradation is a long-term process, while existing RUL prediction models show a limited ability to learn a long-distance dependency. To fill the above research gap, we propose a novel RUL prediction Transformer (RPT) which consists of a tiny convolution-based representation network (RN) and an advanced Transformer feature extractor. In the proposed RPT, the row vibration signals are concisely and efficiently embedded into a tiny feature space by the RN. Then, embedded vectors of historical run-to-failure data are input into the transformer feature extractor to learn potential prediction knowledge. Due to the global attention machine, the RPT can learn long-distance dependency, which significantly improves the RUL prediction. Compared with state-of-the-art models, RPT attains more accurate RUL prediction.
Remaining useful life, Transformer, rolling bearings, information representation, run-to-failure data
gang wang

Gang Wang was born in Dingxi, China. He received the B.S. degree in measurement and control technology and instrument from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2018, the M.S. degree in mechanical engineering from Wenzhou University, Wenzhou, China, in 2021. He is currently pursuing the Ph.D. degree in mechanical engineering at Beijing University of Technology, Beijing, China. His research interests include machinery fault diagnosis, artificial intelligence and signal processing.

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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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