| 唐圳雄,唐东林,丁 超,侯 军.基于 XGBoost 特征重要度的储罐缺陷 ANN 面积量化模型[J].电子测量与仪器学报,2020,34(8):109-115 |
| 基于 XGBoost 特征重要度的储罐缺陷 ANN 面积量化模型 |
| Quantitative model of ANN area of tank defects based on XGBoost feature importance |
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| DOI： |
| 中文关键词: 储罐腐蚀缺陷 面积量化 超声波检测 XGBoost 特征重要度 ANN 模型 |
| 英文关键词:tank corrosion defects area quantization ultrasonic detection XGBoost model ANN |
| 基金项目:四川省科技厅科技支撑计划(2017FZ0033)、成都市技术创新研发项目(2018-YF05-00201-GX)、西南石油大学国家重点实验室项目(PLN201828)资助 |
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| Author | Institution |
| Tang Zhenxiong | 1. Key Laboratory of Petroleum and Natural Gas Equipment, Ministry of Education, Mechanical & Electrical Department, Southwest Petroleum University |
| Tang Dongling | 1. Key Laboratory of Petroleum and Natural Gas Equipment, Ministry of Education, Mechanical & Electrical Department, Southwest Petroleum University |
| Ding Chao | 2. Mechanical Engineering Department,Chengdu Technological University |
| Hou Jun | 1. Key Laboratory of Petroleum and Natural Gas Equipment, Ministry of Education, Mechanical & Electrical Department, Southwest Petroleum University |
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| 摘要点击次数: 548 |
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| 中文摘要: |
| 针对超声波检测的储罐缺陷的面积量化问题,提出一种改进的储罐腐蚀缺陷面积量化模型。 该模型利用 XGBoost 的特
征重要度对人工神经网络(ANN)的参数进行先验初始化实现 ANN 模型的改进。 该模型可以更快的收敛,并且提高准确率。 按
照国家标准设计实验平台,获取实验信号,并提取信号的统计特征得到特征数据集,利用数据集训练和测试改进的模型,并与传
统模型进行对比。 通过实验验证得出,改进的 ANN 模型能够更快的收敛,并且准确量化缺陷面积,相比于 ANN 量化模型,在训
练集上准确率提高了 17. 9%,达到了 98. 3%,在测试集上提高了 16. 6%,达到了 92. 2%。 |
| 英文摘要: |
| In order to solve the problem that quantifying the area of tank defects detected by ultrasonic wave, an improved quantitative
model of tank corrosion defect area is proposed. This model uses the feature importance of XGBoost to initialize the parameters of
artificial neural network (ANN) a priori to improve the ANN model. The model can converge faster and improve the accuracy. Design an
experimental platform according to national standards, obtain experimental signals, and extract the statistical features of the signals to
obtain a data set. Use the data set to train and test improved models, and compare them with traditional models. The experimental
results show that the improved ANN model can converge faster and quantify the defect area accurately. Compared with the ANN
quantization model, the accuracy in the training set has been improved by 17. 9%, reached 98. 3%. and increased by 16. 6% on the test
set, reached 92. 2%. |
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