| 魏新园,周京欢,钱牧云,李丹,黄三傲.随机森林算法在超声缺陷识别中的应用研究[J].电子测量与仪器学报,2024,38(5):47-55 |
| 随机森林算法在超声缺陷识别中的应用研究 |
| Research on the application of random forest algorithmin ultrasonic defect recognition |
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| DOI： |
| 中文关键词: 随机森林 超声探伤 缺陷识别 不同形状和尺寸 分类准确率 |
| 英文关键词:ultrasonic defect detection defect identification random forest different shapes and sizes classification accuracy |
| 基金项目:安徽省重点研究与开发计划项目(2022f04020005)、安徽省高等学校科研研究重点项目(2022AH050313)、安徽省智能破拆装备工程实验室开放基金(2022f04020005)项目资助 |
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| Author | Institution |
| Wei Xinyuan | 1.Anhui Province Engineering Laboratory of Intelligent Demolition Equipment, Ma′anshan 243032,China;
2.School of Electrical and Information Engineering, Anhui University of Technology, Ma′anshan 243032,China |
| Zhou Jinghuan | Anhui Province Engineering Laboratory of Intelligent Demolition Equipment, Ma′anshan 243032,China |
| Qian Muyun | Anhui Province Engineering Laboratory of Intelligent Demolition Equipment, Ma′anshan 243032,China |
| Li Dan | Anhui Province Engineering Laboratory of Intelligent Demolition Equipment, Ma′anshan 243032,China |
| Huang San′ao | Anhui Province Engineering Laboratory of Intelligent Demolition Equipment, Ma′anshan 243032,China |
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| 中文摘要: |
| 超声波检测是一种常见的钢材缺陷检测方法，通过机器学习算法建立分类模型能够实现有效的缺陷识别。神经网络是目前最常采用的一种算法，但存在模型结构复杂且需要大量训练数据的问题。对此，提出一种基于随机森林的超声缺陷识别方法，能够实现对缺陷类型的智能、准确识别，以解决模型结构复杂和训练数据需求大的问题。首先对方体试件中的不同形状、尺寸和深度的缺陷进行超声检测实验，基于实验数据利用随机森林算法建立超声缺陷识别模型；进而对模型的缺陷识别效果进行分析，并与支持向量机、K-近邻分类算法、AdaBoosting算法和卷积神经网络比对分析缺陷识别效果；然后利用验证试件进行缺陷识别验证实验，以进一步验证所建立缺陷识别模型的有效性。结果表明，所提缺陷识别方法相比其他算法具有最高的准确率，验证实验中缺陷分类准确率达到94.6%。 |
| 英文摘要: |
| Ultrasonic detection is a common method of steel defect detection. The classification model established by machine learning algorithm can realize effective defect identification. Neural network is the most commonly used algorithm at present, but it has the problem of complex model structure and large amount of training data. In this paper, an ultrasonic defect recognition method based on random forest is proposed, which can realize intelligent and accurate identification of defect types to solve the problems of complex model structure and large training data requirements. Firstly, ultrasonic detection experiments were carried out for defects of different shapes, sizes and depths in the specimen. Based on the experimental data, an ultrasonic defect recognition model was established using random forest algorithm. Then, the defect recognition effect of the model is analyzed, and compared with support vector machine, K-nearest neighbor classification algorithm, AdaBoosting algorithm and convolutional neural network. Then the defect identification verification experiment is carried out with the verification specimen to further verify the validity of the established defect identification model. The results show that the proposed method has the highest accuracy compared with other algorithms, and the accuracy of defect classification reaches 94.6% in the verification experiment. |
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