Temperature Online Monitoring System for Aerospace Manufacturing Process Based on Gradient Boosting Decision Tree (GBDT) Algorithm

Liliang Wang; Jiaqi Qu; Zheng Qian

doi:10.1109/ICSMD50554.2020.9261752

Temperature Online Monitoring System for Aerospace Manufacturing Process Based on Gradient Boosting Decision Tree (GBDT) Algorithm

Liliang Wang
, Jiaqi Qu
, Zheng Qian^*

^*Corresponding author for this work

School of Instrumentation and Optoelectronic Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The temperature measurement is important in aerospace manufacturing, with the increasing demand for online temperature monitoring. However, it is difficult to obtain high accuracy in traditional temperature detection methods using look-up tables and hardware compensation methods. This paper proposes an online temperature monitoring method for thermocouples based on the gradient boosting decision tree (GBDT) algorithm which enhanced the accuracy of cold end compensation and reduced nonlinear error of thermocouple during online temperature monitoring. Through experimental measurement, it can be seen that the accuracy of thermocouple online temperature monitoring improved by the gradient boosting decision tree algorithm provides a new method for online temperature monitoring in the aerospace manufacturing process.

Original language	English
Title of host publication	International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	587-592
Number of pages	6
ISBN (Electronic)	9781728192772
DOIs	https://doi.org/10.1109/ICSMD50554.2020.9261752
State	Published - 15 Oct 2020
Event	1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Xi'an, China Duration: 15 Oct 2020 → 17 Oct 2020

Publication series

Name	International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings

Conference

Conference	1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020
Country/Territory	China
City	Xi'an
Period	15/10/20 → 17/10/20

Keywords

Thermocouple
aerospace manufacturing
gradient boosting decision tree
temperature

Access to Document

10.1109/ICSMD50554.2020.9261752

Cite this

Wang, L., Qu, J., & Qian, Z. (2020). Temperature Online Monitoring System for Aerospace Manufacturing Process Based on Gradient Boosting Decision Tree (GBDT) Algorithm. In International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings (pp. 587-592). Article 9261752 (International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSMD50554.2020.9261752

Wang, Liliang ; Qu, Jiaqi ; Qian, Zheng. / Temperature Online Monitoring System for Aerospace Manufacturing Process Based on Gradient Boosting Decision Tree (GBDT) Algorithm. International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 587-592 (International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings).

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title = "Temperature Online Monitoring System for Aerospace Manufacturing Process Based on Gradient Boosting Decision Tree (GBDT) Algorithm",

abstract = "The temperature measurement is important in aerospace manufacturing, with the increasing demand for online temperature monitoring. However, it is difficult to obtain high accuracy in traditional temperature detection methods using look-up tables and hardware compensation methods. This paper proposes an online temperature monitoring method for thermocouples based on the gradient boosting decision tree (GBDT) algorithm which enhanced the accuracy of cold end compensation and reduced nonlinear error of thermocouple during online temperature monitoring. Through experimental measurement, it can be seen that the accuracy of thermocouple online temperature monitoring improved by the gradient boosting decision tree algorithm provides a new method for online temperature monitoring in the aerospace manufacturing process.",

keywords = "Thermocouple, aerospace manufacturing, gradient boosting decision tree, temperature",

author = "Liliang Wang and Jiaqi Qu and Zheng Qian",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 ; Conference date: 15-10-2020 Through 17-10-2020",

year = "2020",

month = oct,

day = "15",

doi = "10.1109/ICSMD50554.2020.9261752",

language = "英语",

series = "International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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address = "美国",

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Wang, L, Qu, J & Qian, Z 2020, Temperature Online Monitoring System for Aerospace Manufacturing Process Based on Gradient Boosting Decision Tree (GBDT) Algorithm. in International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings., 9261752, International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 587-592, 1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020, Xi'an, China, 15/10/20. https://doi.org/10.1109/ICSMD50554.2020.9261752

Temperature Online Monitoring System for Aerospace Manufacturing Process Based on Gradient Boosting Decision Tree (GBDT) Algorithm. / Wang, Liliang; Qu, Jiaqi; Qian, Zheng.
International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 587-592 9261752 (International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Temperature Online Monitoring System for Aerospace Manufacturing Process Based on Gradient Boosting Decision Tree (GBDT) Algorithm

AU - Wang, Liliang

AU - Qu, Jiaqi

AU - Qian, Zheng

PY - 2020/10/15

Y1 - 2020/10/15

N2 - The temperature measurement is important in aerospace manufacturing, with the increasing demand for online temperature monitoring. However, it is difficult to obtain high accuracy in traditional temperature detection methods using look-up tables and hardware compensation methods. This paper proposes an online temperature monitoring method for thermocouples based on the gradient boosting decision tree (GBDT) algorithm which enhanced the accuracy of cold end compensation and reduced nonlinear error of thermocouple during online temperature monitoring. Through experimental measurement, it can be seen that the accuracy of thermocouple online temperature monitoring improved by the gradient boosting decision tree algorithm provides a new method for online temperature monitoring in the aerospace manufacturing process.

AB - The temperature measurement is important in aerospace manufacturing, with the increasing demand for online temperature monitoring. However, it is difficult to obtain high accuracy in traditional temperature detection methods using look-up tables and hardware compensation methods. This paper proposes an online temperature monitoring method for thermocouples based on the gradient boosting decision tree (GBDT) algorithm which enhanced the accuracy of cold end compensation and reduced nonlinear error of thermocouple during online temperature monitoring. Through experimental measurement, it can be seen that the accuracy of thermocouple online temperature monitoring improved by the gradient boosting decision tree algorithm provides a new method for online temperature monitoring in the aerospace manufacturing process.

KW - Thermocouple

KW - aerospace manufacturing

KW - gradient boosting decision tree

KW - temperature

UR - https://www.scopus.com/pages/publications/85098591733

U2 - 10.1109/ICSMD50554.2020.9261752

DO - 10.1109/ICSMD50554.2020.9261752

M3 - 会议稿件

AN - SCOPUS:85098591733

T3 - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings

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BT - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020

Y2 - 15 October 2020 through 17 October 2020

ER -

Wang L, Qu J, Qian Z. Temperature Online Monitoring System for Aerospace Manufacturing Process Based on Gradient Boosting Decision Tree (GBDT) Algorithm. In International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 587-592. 9261752. (International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings). doi: 10.1109/ICSMD50554.2020.9261752

Temperature Online Monitoring System for Aerospace Manufacturing Process Based on Gradient Boosting Decision Tree (GBDT) Algorithm

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