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基于Gentle Adaboost的气密性检测系统

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差压法气密性检测易受外部因素与预设参数影响.针对问题基于集成学习建立气密性检测系统,包含传感器终端数据采集系统、人机交互界面,并用最小二乘法对传感器进行线性拟合,利用Gentle Adaboost算法寻找每轮迭代中最佳弱分类器并更新下一轮样本权重,通过集成数轮迭代中最佳弱分类器组成强分类器,对被测物的气密性能进行判断.实验结果表明:所提系统在气密性检测中的准确度、精确度与召回率皆优于传统方法与单一分类模型,准确度达到99.8％,能有效克服外部因素对检测结果的影响,提高了差压法气密性检测的准确性与稳定性.
Air-Leakage Detection System Based on Gentle Adaboost
The differential pressure air-leakage detection is easily affected by external factors and preset parameters.Aiming at the problem,an air-leakage detection system based on ensemble learning was established,which included the sensor terminal data acquisi-tion system,human-computer interface,and linear fitting of the sensor with the least square method.The Gentle Adaboost algorithm was used to find the best weak classifier in each iteration and update the sample weight in the next round.A strong classifier was formed by integrating the best weak classifier in several rounds of iterations to judge the sealing performance of the tested object.The experimental results show that the accuracy,precision and recall of the proposed system in air-leakage detection are superior to traditional methods and single classification model,and the accuracy is 99.8％.It can effectively overcome the influence of external factors on the test re-sults,and improve the accuracy and stability of differential pressure air-leakage detection.

air-leakage detectiondifferential pressure methodclassifierintegrated learningGentle Adaboost algorithm

张梓齐、耿乐陶、李阳、杨正乐、郭子兴、胡敏、庄正飞

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华南师范大学生物光子学研究院,广东广州 510631

华南师范大学(清远)科技创新研究院,广东清远 511517

师大瑞利光电科技(清远)有限公司,广东清远 511517

气密性检测 差压法 分类器 集成学习 Gentle Adaboost算法

国家自然科学基金面上项目国家自然科学基金重点项目广州市科技计划清远高新区人才项目清远高新区特色载体建设项目

6187505662135003202201010704202197号2150805

2024

10.3969/j.issn.1001-3881.2024.04.015

机床与液压
中国机械工程学会 广州机械科学研究院有限公司

机床与液压

CSTPCD北大核心
影响因子：0.32
ISSN：1001-3881
年,卷(期)：2024.52(4)
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