Enhanced bearing RUL prediction based on dynamic temporal attention and mixed MLP

Jin, Zhongtian, Chen, Chong, Syntetos, Aris ORCID: https://orcid.org/0000-0003-4639-0756 and Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 2025. Enhanced bearing RUL prediction based on dynamic temporal attention and mixed MLP. Autonomous Intelligent Systems 5 (1) , 2. 10.1007/s43684-024-00088-4

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Abstract

Bearings are critical components in machinery, and accurately predicting their remaining useful life (RUL) is essential for effective predictive maintenance. Traditional RUL prediction methods often rely on manual feature extraction and expert knowledge, which face specific challenges such as handling non-stationary data and avoiding overfitting due to the inclusion of numerous irrelevant features. This paper presents an approach that leverages Continuous Wavelet Transform (CWT) for feature extraction, a Channel-Temporal Mixed MLP (CT-MLP) layer for capturing intricate dependencies, and a dynamic attention mechanism to adjust its focus based on the temporal importance of features within the time series. The dynamic attention mechanism integrates multi-head attention with innovative enhancements, making it particularly effective for datasets exhibiting non-stationary behaviour. An experimental study using the XJTU-SY rolling bearings dataset and the PRONOSTIA bearing dataset revealed that the proposed deep learning algorithm significantly outperforms other state-of-the-art algorithms in terms of RMSE and MAE, demonstrating its robustness and accuracy.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics) Engineering
Publisher:	Springer Nature
ISSN:	2730-616X
Date of First Compliant Deposit:	14 January 2025
Date of Acceptance:	19 December 2024
Last Modified:	20 Jan 2025 10:45
URI:	https://orca.cardiff.ac.uk/id/eprint/175281

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