Intelligent evaluation of acoustic emission detection of tank floor corrosionbased on SMOTE-GWO-SVM model
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摘要: 将储罐宏观特征和声发射特征相结合,以“可能的腐蚀状况”为导向对储罐宏观数据进行处理。同时针对储罐声发射判级数据样本数量少、分布不平衡的情况,采用合成少数类过采样技术(SMOTE)和灰狼算法优化支持向量机(GWO-SVM)相结合的模型进行储罐的安全状态等级智能预测。结果表明该模型能有效提高小样本、不平衡数据识别的准确率和可靠性。Abstract: By the combination of the macro characteristics and acoustic emission characteristics of storage tanks, the macro data processing of storage tanks is guided by the possible corrosion conditions. At the same time, in view of the small number of samples and unbalanced distribution of acoustic emission grading data of storage tanks, a model combining the synthetic minority oversampling technique (SMOTE) and gray wolf optimizer-based support vector machine (GWO-SVM) is used to intelligently predict the safety state grade of storage tanks. The results show that this model can effectively improve the accuracy and reliability of identifying small samples and unbalanced data.
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Keywords:
- acoustic emission /
- tank floor corrosion /
- SMOTE /
- GWO-SVM /
- intelligent evaluation
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