State recognition for fuselage join based on gradient boosting tree

doi:10.3785/j.issn.1008-973X.2019.07.005

Journal of ZheJiang University (Engineering Science)

2019, Vol. 53

Issue (7): 1274-1281 DOI: 10.3785/j.issn.1008-973X.2019.07.005

Mechanical and Energy

State recognition for fuselage join based on gradient boosting tree

Chang CAI1(

),Yi-xiang HUANG1,*(

),Hong-wen XING2

1. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
2. Institute of Aeronautical Manufacturing Technology, Shanghai Aircraft Manufacturing Limited Company, Shanghai 200436, China

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Abstract

A state recognition method for fuselage join based on gradient boosting decision tree (GBDT) was proposed by considering the practical conditions of the lack of label and sample imbalance of fuselage joining data in order to monitor the process of fuselage join in real time. The displacement and the load data were acquired in real time through the positioners and the load sensors during the process of fuselage join. The joining state of historical data was labeled based on the process of airliner component join, and an accurate and efficient automatic labeling method for fuselage joining state was proposed. The state recognition model for fuselage join based on GBDT was trained through the labeled data, from which the importance of each feature was obtained. The macro_F₁ for joining state recognition of the proposed method was as high as 0.998, compared with the latest deep learning methods such as long short-term memory (LSTM), convolutional neural network (CNN) and some traditional machine learning methods. Each joining state was accurately recognized, and the model training process was more efficient.

Key words： fuselage join state labeling state recognition data driven gradient boosting decision tree (GBDT) unbalanced multi-classification

Received: 23 July 2018 Published: 25 June 2019

CLC:

TP 274

Corresponding Authors: Yi-xiang HUANG E-mail: ccuniquelife@sjtu.edu.cn;huang.yixiang@sjtu.edu.cn

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Cite this article:

Chang CAI,Yi-xiang HUANG,Hong-wen XING. State recognition for fuselage join based on gradient boosting tree. Journal of ZheJiang University (Engineering Science), 2019, 53(7): 1274-1281.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.07.005 OR http://www.zjujournals.com/eng/Y2019/V53/I7/1274

基于梯度提升树的飞机机身对接状态识别

为了实时监控飞机机身的对接过程，针对机身对接数据没有标注和样本不平衡的特点，提出基于梯度提升树（GBDT）的机身对接状态识别方法. 通过定位器及定位器上的载荷传感器，实时获取机身对接过程中的位移和载荷数据. 结合飞机部件对接的工艺流程对历史对接数据进行状态标注，提出准确、高效的对接状态自动标注方法. 在经过标注的对接数据上训练基于GBDT的机身对接状态识别模型，通过该模型可以获得各个特征的重要性. 与长短期记忆网络（LSTM）、卷积神经网络（CNN）以及一些传统机器学习方法相比，该方法对接状态识别的宏F₁（macro_F₁）指标高达0.998，能够精准地识别每一种对接状态且训练速度较快.

关键词： 机身对接, 状态标注, 状态识别, 数据驱动, 梯度提升树（GBDT）, 不平衡多分类

Fig.1 Sketch map of ensemble learning

Fig.2 Joining principle of aircraft components

Fig.3 Automatic joining process of components

Fig.4 Diagram of join between front and middle fuselage

Fig.5 Boxplot of each feature

Fig.6 Variations of S2LAPOS_X during posture alignment

Fig.7 Variations of Y-direction force during fuselage join

Tab.1 Sample size of each joining state for differentlabeling methods

Fig.8 Euclidean distance between current sample and previous one

Fig.9 Sample size of each joining state

Tab.2 Confusion matrix of classification results

Fig.10 Confusion matrix of GBDT model

Fig.11 Feature importance of GBDT model

Tab.3 Comparison of evaluation indexes of each model

Fig.12 F₁ of each model


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