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.
|Number of pages||10|
|Journal||International Journal of Performability Engineering|
|State||Published - 1 Jan 2019|
Bibliographical noteFunding 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).
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- Evolutionary extreme learning machine
- Extreme learning machine
- Fault diagnosis
- Level-based learning swarm particle