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首页> 外文会议>International Seminar on Electron Devices Design and Production >The Method of Diagnostics of Radio-Electronic Means Based on the Analysis of Shock Effects by Means of Machine Learning Algorithms
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The Method of Diagnostics of Radio-Electronic Means Based on the Analysis of Shock Effects by Means of Machine Learning Algorithms

机译：基于机器学习算法分析的基于对冲击效应分析的无线电电子装置的诊断方法

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The content of the article focuses on the problem of insufficient effectiveness of existing methods of diagnostics of radio-electronic means, in particular, aimed at identifying structural defects. The defects of mechanical nature are not given sufficient attention, so the development of a diagnostic system aimed at identifying structural defects is an urgent task of practical significance. To solve this problem, it is proposed to use modern information technologies, including machine learning algorithms, which in turn will be implemented in the developed methodology for diagnosing radio-electronic means based on the analysis of impact impacts and which in turn will involve artificial neural networks based on the recognition of images of structural defects obtained after mechanical impacts. The paper describes in detail the specified diagnostic method, determines the configuration of the artificial neural network and calculates its accuracy, which in turn allows us to conclude that the chosen approach is promising. The paper presents a project of an automated system for diagnosing radio-electronic means based on the analysis of shock effects using neural networks, defines its architecture, designs the main modules, and determines the types of software.
机译：文章的内容侧重于无线电装置诊断方法的有效性不足的问题，特别是旨在识别结构缺陷。机械性质的缺陷没有足够的注意，因此旨在识别结构缺陷的诊断系统的发展是一种实际意义的紧迫任务。为了解决这个问题，建议使用现代信息技术，包括机器学习算法，其又将在开发方法中实现，以基于对冲击影响的分析诊断无线电电子装置，并且又将涉及人工神经网络基于识别机械冲击后获得的结构缺陷图像的网络。本文详细介绍了指定的诊断方法，确定了人工神经网络的配置，并计算了其准确性，这反过来允许我们得出结论，所选择的方法是有前途的。本文提出了一个自动化系统的项目，用于诊断基于使用神经网络的冲击效果的分析，定义其架构，设计主模块，并确定软件的类型。

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