ACFM online defect determination method based on LSTM
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摘要: 交流电磁场检测（ACFM）技术在进行缺陷判定时，存在检测数据追溯、现场判定缺陷困难等问题。分析了ACFM检测信号特征，开发了一种部署在云服务器上的在线数据存储、检测信息显示以及缺陷智能判定的方法。该系统主要由检测仪与云端服务器组成，检测时仪器采集检测信号，将检测信息实时传输至云服务器，云服务器存储检测信息并通过网页显示，同时基于长短期记忆神经网络（LSTM）的缺陷判定算法分析检测信息并返回结果至检测仪。以铝板试件作为检测对象，对系统进行功能测试。试验结果表明，开发的在线缺陷判定算法实现了交流电磁场检测系统数据存储、信息查看、缺陷判定的目标。Abstract: When the alternating current field measurement(ACFM) technology is used for defect determination, there are some problems such as traceability of detection data and difficulty in determining defects on site. The characteristics of ACFM inspection signals is analyzed in this thesis, and the method is proposed which deploys online data storage, inspection information display, and intelligent determination of defects method on the cloud server. The detection system is mainly composed of a detector and a cloud server. The instrument collects detection signals and transmits the detection information to the cloud server in real time. The cloud server stores the detection information and displays it on the web. At the same time, the detection information is analyzed by the defect judgment algorithm based on long short-term memory neural network (LSTM), and the results are is returned to the detector. Taking the aluminum plate specimen as the detection object, the functional test of the experimental system is carried out. The experimental results show that the developed online defect determination algorithm achieves the goals of data storage, information viewing and defect determination in the ACFM detection system.
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