计量学报2026，Vol.47Issue(5)：686-692,7.DOI:10.3969/j.issn.1000-1158.2026.05.07
基于小样本量的超声流量计使用中检验模型构建与优化
Construction and Optimization of In-service Inspection Model for Ultrasonic Flowmeter Based on Small Sample Size
摘要
Abstract
To address the problems of large sample size,long test cycle and low efficiency in the model construction of existing machine learning-based in-service inspection models for ultrasonic flowmeters,backpropagation(BP)neural network and random forest models under different sample sizes were established based on experimental data from the national urban gas flow standard device(uncertainty 0.26%,k=2).A small sample set was obtained by halving the initial sample set(time interval 6 s,30 s)using the arithmetic mean method.The results show that the prediction performance(evaluated by R² and root mean square erro)of both models under small sample size is lower than that of the initial sample size model,and the BP neural network has an overall better performance.To improve the performance of the small sample model,three feature optimization algorithms(ReliefF,Recursive Feature Elimination,and random forest feature algorithm)were adopted.The results indicate that the random forest feature algorithm achieves the optimal optimization effect,with the maximum improvement of prediction accuracy reaching 48.65%(at 884 m³/h flow point).The optimized small sample model retains 9～14 key features,and its prediction performance is comparable to that of the initial sample size model,with significantly improved modeling efficiency.Field application verification shows that the predicted indication error of the model is stable within±1%,which can meet the in-service inspection requirements of ultrasonic flowmeters in natural gas transmission stations.关键词
流量计量/超声流量计/使用中检验/机器学习/小样本量模型/特征优化Key words
flow measurement/ultrasonic flowmeter/in-service inspection/machine learning/model with small sample size/feature optimization分类
通用工业技术引用本文复制引用
何煜迪,李梦娜,李春辉,徐雅,谢代梁..基于小样本量的超声流量计使用中检验模型构建与优化[J].计量学报,2026,47(5):686-692,7.基金项目
中国计量科学研究院基本科研业务费重点领域项目(AKYZD2406-1) （AKYZD2406-1）
