Intelligent Fault Diagnosis of Delta 3D Printers using Attitude Sensors based on Extreme Learning Machines

doi:10.23940/ijpe.19.12.p11.31963208

Abstract

Abstract: The influence of intelligent fault diagnosis on industrial development is becoming more and more important. In order to study the fault diagnosis technique of delta 3D printers using extreme learning machine (ELM), a low-cost attitude sensor was used in our designed machine. In the research process, the cross validation method was used to train ELM to obtain the optimal model. Through the analysis of different activation functions, we found that the correct recognition rates corresponding to the same activation function are different, and there are great differences among training samples and fault categories. The sin function, mexihat function, and tribas function recognition effects were better. The analysis of different activation functions revealed that the correct recognition rates corresponding to the same activation function are different, and there are great differences in different training samples and fault categories.

Key words: fault diagnosis, extreme learning machine, activation function, delta 3D printer

Xiaoyan Li, Jianwen Guo, Xuejun Jia, Shaohui Zhang, and Zhiyuan Liu. Intelligent Fault Diagnosis of Delta 3D Printers using Attitude Sensors based on Extreme Learning Machines [J]. Int J Performability Eng, 2019, 15(12): 3196-3208.

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