Publication

Remaining useful life prediction of rolling bearings based on adaptive continuous deep belief networks and improved kernel extreme learning machine

Journal Article (2025)

Journal

International Journal of Adaptive Control and Signal Processing

Pages

1533-1547

Volume

39

Number

7

Doc link

https://doi.org/10.1002/acs.3908

File

Download the digital copy of the doc pdf document

Authors

Abstract

Rolling bearings are crucial components in a wide variety of machinery. Monitoring their conditions and predicting their remaining useful life (RUL) is vital to prevent unexpected breakdowns, optimize maintenance schedules, and reduce operational costs. This article proposes an approach based on adaptive continuous deep belief networks (ACDBN) and improved kernel extreme learning machine (KELM) to predict the RUL of rolling bearings. In the proposed approach, the ACDBN model is used for extracting hidden fault features and the distance between the initial health state and the real-time degradation state is used to construct a health indicator (HI). Then, a hybrid kernel extreme learning machine prediction model optimized by the sparrow search algorithm (SSA-KELM) is proposed to estimate the RUL using the extracted HIs. The SSA is used to find the optimal parameters of the KELM model. The proposed method has been assessed using existing bearing datasets. The obtained results indicate that the proposed method successfully improves RUL prediction accuracy compared to existing approaches in the literature.

Categories

control theory.

Scientific reference

M. Zhou, J. Wang, Y. Shi, Z. Wang and V. Puig. Remaining useful life prediction of rolling bearings based on adaptive continuous deep belief networks and improved kernel extreme learning machine. International Journal of Adaptive Control and Signal Processing, 39(7): 1533-1547, 2025.