A novel doublet extreme learning machines for Delta 3D printer fault diagnosis using attitude sensor

Jianwen Guo; Xiaoyan Li; Zhiyuan Liu; Shaohui Zhang; Jiapeng Wu; Chuan Li; Jianyu Long

doi:10.1016/j.isatra.2020.10.024

A novel doublet extreme learning machines for Delta 3D printer fault diagnosis using attitude sensor

ISA Trans. 2021 Mar:109:327-339. doi: 10.1016/j.isatra.2020.10.024. Epub 2020 Oct 17.

Authors

Jianwen Guo¹, Xiaoyan Li², Zhiyuan Liu³, Shaohui Zhang⁴, Jiapeng Wu¹, Chuan Li¹, Jianyu Long¹

Affiliations

¹ School of Mechanical Engineering, Dongguan University of Technology, Dongguan, 523808, China.
² School of Mechanical Engineering, Dongguan University of Technology, Dongguan, 523808, China; College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, 518060, China.
³ College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, 518060, China.
⁴ School of Mechanical Engineering, Dongguan University of Technology, Dongguan, 523808, China. Electronic address: zhangsh@dgut.edu.cn.

PMID: 33092861
DOI: 10.1016/j.isatra.2020.10.024

Abstract

Extreme learning machine (ELM) has better operation efficiency in fault diagnosis. However, the recognition accuracy of ELM algorithm is actually affected by the activation function. Moreover, most of the testing dataset are coming from high precision and expensive sensors. In this paper, raw data are collected by a low-cost attitude sensor, which is installed on the mobile platform of a delta 3D printer. A doublet activation function is proposed to improve the performance of ELM, named doublet ELM (DELM). The proposed method is evaluated using experimental data collected from the 3D printer, and its advantages are demonstrated by comparing with other activation functions. The experimental results indicate that the proposed method leads to the highest accuracy in different hidden nodes and the testing classification rate achieves 93% and 96% using only 8.33% of the dataset for model training, for R75 and R90 sub-datasets, respectively. Moreover, compared with peer methods, such as random forest, echo state network, and so on, the results show that the present DELM exhibits the best performance in small-sample and improves the accuracy of the 3D printer fault diagnosis.

Keywords: Activation function; Delta 3D printer; Extreme learning machine; Fault diagnosis.