High-accuracy gearbox health state recognition based on graph sparse random vector functional link network

Xin Li, Yu Yang, Zhantao Wu, Ke Yan, Haidong Shao and Junsheng Cheng

Reliability Engineering and System Safety, 2022, vol. 218, issue PB

Abstract: Health state recognition technology is of great significance for the maintenance decision-making and safety assessment of gearboxes. Traditional random vector functional link network (RVFLN) cannot fully leverage the label sparsity of gearbox health states and the manifold structure information of raw data is neglected. To overcome these drawbacks, a high-accuracy gearbox health state recognition model is proposed in this paper, namely graph sparse RVFLN (GSRVFLN). Firstly, a sparse constraint term is introduced into GSRVFLN to force the predicted label to be similar to the zero-one true label as much as possible, so as to fully exploit the sparsity of the output label. Secondly, a discriminative adjacency graph is designed for GSRVFLN with the label information to capture the inherent geometry structure and discriminative information of data. Finally, we derive an effective solution for GSRVFLN with the alternating direction method of multipliers (ADMM) framework, and this solution has great convergence. The applicability of GSRVFLN for health state recognition is validated with two gearbox datasets, and comparative results show that GSRVFLN achieves the excellent performance of gearbox health state recognition, winning other state-of-the-art models.

Keywords: Random vector functional link network; Sparse constraint; Discriminative information; Health state recognition; Gearbox fault diagnosis (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832021006712
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:218:y:2022:i:pb:s0951832021006712

DOI: 10.1016/j.ress.2021.108187

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().