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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)2972-2981
Number of pages10
JournalInternational Journal of Performability Engineering
Volume15
Issue number11
DOIs
StatePublished - 1 Jan 2019

Bibliographical note

Funding Information:
This work is supported in part by the National Natural Science Foundation of China (No. 71801046, 51775112, and 51605406), Natural Science Foundation of Guangdong Province (No. 2018A030310029), Youth Innovative Talent Project (No. 2017KQNCX191) from the Department of Education of Guangdong Province, DGUT Research Project (KCYKYQD2017011), and Research Start-up Funds of DGUT (No. GC300501-12 and GC300501-26).

Publisher Copyright:
© 2019 Totem Publisher, Inc. All rights reserved.

Keywords

  • Evolutionary extreme learning machine
  • Extreme learning machine
  • Fault diagnosis
  • Level-based learning swarm particle
  • Metaheuristic

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