An adaptive dual distillation framework for efficient remaining useful life prediction

An adaptive dual distillation framework for efficient remaining useful life prediction

Abstract Predicting the Remaining Useful Life (RUL) of industrial equipment is essential for proactive maintenance and health assessment, particularly under the computational constraints of edge devices. While deep learning methods, such as Long Short-Term Memory (LSTM) networks, excel at modeling c...

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Bibliographic Details
Main Authors: Xiang Cheng, Jun Kit Chaw, Shafrida Sahrani, Mei Choo Ang, Saraswathy Shamini Gunasekaran, Moamin A. Mahmoud, Halimah Badioze Zaman, Yanfeng Zhao, Fuchen Ren
Format: Article
Language:English
Published: Springer 2025-04-01
Series:Complex & Intelligent Systems
Subjects:
Remaining useful life prediction
Gated recurrent unit
Correlation-based feature alignment
Adaptive
Knowledge distillation
Online Access:https://doi.org/10.1007/s40747-025-01886-w
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https://doi.org/10.1007/s40747-025-01886-w

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