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

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

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.

Suggested Citation

  • Li, Xin & Yang, Yu & Wu, Zhantao & Yan, Ke & Shao, Haidong & Cheng, Junsheng, 2022. "High-accuracy gearbox health state recognition based on graph sparse random vector functional link network," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    • Handle: RePEc:eee:reensy:v:218:y:2022:i:pb:s0951832021006712
    DOI: 10.1016/j.ress.2021.108187
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    References listed on IDEAS

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