The paper presents the influence of low alloy steel degradation on the acoustic emission (AE) generated during static tension of notched specimen. The material was cut from a technological pipeline long-term operated in the oil refinery industry. Comparative analysis of AE activity generated by damage process of degraded and new material has been carried out. The different AE parameters were used to detect different stages of fracture process of low alloy steel under quasi-static tensile test. Neural networks with three layers were created with Broyden–Fletcher–Goldfarb–Shanno learning algorithm for a database analysis. The different AE parameters were included in the input layer. Classification neural networks were created in order to determine the stages of material degradation. The results obtained from the carried out studies will be used as the basis for new methodology development of the assessment of the structural condition of in-service equipment.

中文翻译：

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分类神经网络在声发射数据分析中识别退化低合金钢损伤阶段的应用

本文介绍了低合金钢的降解对缺口试样静态拉伸过程中产生的声发射（AE）的影响。该材料是从长期在炼油厂中运行的技术管道中切割出来的。对降解和新材料的破坏过程产生的AE活性进行了比较分析。在准静态拉伸试验下，采用不同的声发射参数来检测低合金钢断裂过程的不同阶段。使用Broyden-Fletcher-Goldfarb-Shanno学习算法创建了三层神经网络，用于数据库分析。不同的AE参数包含在输入层中。创建分类神经网络以确定材料降解的阶段。