Kohonen’s algorithm in problems of classification of defects in printed circuit assemblies

The article presents a new method for diagnosing the technical condition of radio-electronic components, combining the methods of thermal diagnostics with the technologies of artificial neural networks. The structure of the method is shown, and the composition of the functional blocks is determined. The implementation of the method is a symbiosis of technologies for mathematical and simulation modeling of the technical state of a radio-electronic device with its physical tests and research of characteristics. When developing the method, specialized software tools for design and circuit design were actively used, such as Altium Designer CAD, SolidWorks, NI Multisim, the FloTHERM PCB thermal analysis module, as well as the MATLAB mathematical modeling and calculation package. With the help of these tools, a number of studies were carried out, including sets of numerical values of the power of circuit elements and temperature indicators of the printing unit, both for the correct state of the device and in states with artificially introduced defects. They, in turn, became the basis of the database of electronic node failures. To implement diagnostic procedures and identify the technical condition, an artificial neural network based on selforganizing Kohonen maps was created, its structure, parameters and algorithms of functioning were determined. The diagnostic procedure is based on the analysis of information from the fault database and its comparison with experimental data obtained as a result of a physical experiment. The results of the study showed that the network automatically classifies the characteristic defects of electronic components using the algorithms embedded in it. The list of characteristic defects in the proposed diagnostic method is limited to a discrete set of the most common faults, because, as their number increases, the use of the self-organizing Kohonen network for automatic classification becomes much more complicated and ineffective in terms of performance and reliability of identification. Among the advantages of this technology, it should be noted that the Kohonen network has the ability to convert largedimensional input data into a two-dimensional array. So, the results are easy to visualize and convenient to use when generating reports and recommendations for subsequent decision-making about the possibility of using an electronic device.

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Artificial neural network in the diagnostic problem of structural defects of printed components in electronic means

Neurocomputers ◽

10.18127/j19998465-202010-04 ◽

2021 ◽

Author(s):

S.U. Uvajsov ◽

V.V. Chernoverskaya ◽

S.M. Lyshov ◽

Fam Le Kuok Han ◽

A.S. Uvajsova

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Monte Carlo ◽

Electronic Devices ◽

Diagnostic Procedures ◽

Diagnostic Methods ◽

Test Method ◽

Radio Electronic ◽

Artificial Neural ◽

Production And Operation

Problem statement. Modern radio-electronic means (RES) are complex technical systems that have found application in almost all industries and spheres of human activity. The wide functionality of RES often leads to a complication of their constructive implementation, and, as a result, to the complexity and ambiguity of diagnostic procedures performed during production and operation. In this regard, the issue of improving existing methods of technical control and developing new approaches to the diagnosis of RES in order to identify their hidden defects and increase the reliability of research results is quite acute. Goal. Improving the efficiency of diagnosing printed circuit assemblies of electronic devices in the process of their production, final inspection, testing and intended use. Research methods. At the initial stage of the study, a computer model of the printing unit under study was developed, containing detailed information about the device design. Then we analyzed the most common types of defects in printed components that occur during the production and operation of electronic devices. Seven characteristic defects were identified. Since each defect changed the type of dynamic response characteristics of the object under study, the amplitude-time characteristics of the printing unit were formed for the correct state of the device and for States with defects. Using the Monte Carlo method, a series of samples with acceptable ranges of parameter values was created for each defect. From the obtained samples (sets of amplitude characteristics of the investigated node), a fault database was formed, which was used as a comparison with the sample in diagnostic procedures. Next, a 3-layer artificial neural network (ins) was created, which was trained and tested on samples from the fault database. The results of training the ins based on activation functions allowed us to conclude that it has achieved the required level of pattern recognition and the specified reliability of the results obtained. Results. In the course of the study, a database of characteristic electronic failures was developed, for which, along with a physical experiment, mathematical modeling methods and the Monte Carlo statistical test method were used. In addition, an artificial neural network was created, which became the main tool for diagnostic research in order to detect defects in the electronic node and significantly increased the reliability of the results in comparison with existing diagnostic methods. Practical significance. To test the developed method, a series of computational experiments was performed. The type of test impact in the form of a sawtooth pulse with a linearly increasing leading edge was justified, and the parameters of this pulse were selected by calculation. The artificial neural network training technology allowed us to obtain reliable diagnostic results with a probability of P=0.99. The computational experiment was confirmed by physical tests of the radio-electronic unit on a vibration shock installation.

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Classification of Cardiac Signals Using Artificial Neural Network and Fuzzy Equivalence Relation

i-manager’s Journal on Software Engineering ◽

10.26634/jse.2.4.496 ◽

2008 ◽

Vol 2 (4) ◽

pp. 48-56

Author(s):

B. Anuradha ◽

V.C. Veera Reddy

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Equivalence Relation ◽

Artificial Neural ◽

Cardiac Signals

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Classification of shapes of electronic components for placement on printed wiring boards

10.3403/00185172u ◽

2015 ◽

Keyword(s):

Printed Wiring Boards ◽

Electronic Components

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Classification of shapes of electronic components for placement on printed wiring boards

10.3403/00185172 ◽

1988 ◽

Keyword(s):

Printed Wiring Boards ◽

Electronic Components

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Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

10.36652/0042-4633-2020-8-61-64 ◽

2020 ◽

pp. 61-64

Author(s):

Yu.G. Kabaldin ◽

A.A. Khlybov ◽

M.S. Anosov ◽

D.A. Shatagin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Acoustic Emission ◽

Acoustic Emission Signal ◽

Low Temperatures ◽

Cold Resistance ◽

Emission Signal ◽

Impact Bending ◽

Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

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