Improving extreme learning machine by a level-based learning swarm optimizer and its application to fault diagnosis of 3d printers

Jianyu Long, Ying Hong, Shaohui Zhang, Diego Cabrera, Jingjing Zhong

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

1 Cita (Scopus)

Resumen

Fault diagnosis plays a significant role in the printing quality for 3D printers. In this paper, an extreme learning machine based on level-based learning swarm optimizer (LLSO-ELM) is proposed to diagnose faults of delta 3D printers. Extreme learning machine (ELM) achieves better performance in learning speed than traditional gradient descent algorithms. However, the random inputs weights and hidden biases are influential factors for the accuracy and generalization performance of ELM. LLSO has competitive performance in solution quality and computational efficiency for large scale optimization problems, and it is used to obtain the optimum configuration of the weights and biases for ELM. The proposed model is tested by using the attitude data of a delta 3D printer under different operating modes. The experimental results verify that the proposed approach performs better in generalization and stability than ELM.

Idioma originalInglés
Páginas (desde-hasta)2972-2981
Número de páginas10
PublicaciónInternational Journal of Performability Engineering
Volumen15
N.º11
DOI
EstadoPublicada - 1 ene. 2019

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