Multi-feature Fused Bidirectional Long Short-term Memory for Remaining Useful Life Prediction

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Multi-feature Fused Bidirectional Long Short-term Memory for Remaining Useful Life Prediction
Title:
Multi-feature Fused Bidirectional Long Short-term Memory for Remaining Useful Life Prediction
Other Titles:
2021 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Keywords:
Publication Date:
11 January 2022
Citation:
Jin, R., Chen, Z., Wu, K., Wu, M., Li, X., & Yan, R. (2021). Multi-feature Fused Bidirectional Long Short-term Memory for Remaining Useful Life Prediction. 2021 International Conference on Sensing, Measurement & Data Analytics in the Era of Artificial Intelligence (ICSMD). doi:10.1109/icsmd53520.2021.9670768
Abstract:
In industry, prognostic health management (PHM) is used to improve the system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a necessary role in preventing machine failure and lowering operation cost. Recently, benefitted from deep learning technology development, many RUL prediction approaches are proposed by using long short-term memory (LSTM) or convolutional neural networks (CNN). There methods show impressive performances. However, existing deep learning based methods directly utilize raw signals. Affected by noise in the raw input, the feature representation is degraded, further degenerating the prediction accuracy. To address this issue, a multi-feature fused bidirectional LSTM (MF-LSTM) is proposed. Our proposed MF-LSTM consists of two part: multi-feature fusion (MF) module and multi-head attentive fusion (MA) module. In MF module, feature extracted by a bidirectional LSTM is combined with traditional handcrafted features. A fusion layer is proposed in MF module, which effectively combines both features and improves the feature representation. Furthermore, an attention module is proposed according to multi-head attention mechanism, which improves the performance further. To verify our MF-LSTM performance, experiments are carried out on the C-MAPSS dataset, showing a state-of-the-art performance.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*star - learning with less data
Grant Reference no. : A20H6b0151
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
ISBN:
978-1-6654-2747-0
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