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Training of an artificial neural network in the diagnostic system of a technical object

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Abstract

The present paper covers the issue of training of an artificial neural network in an intelligent diagnostic system whose purpose is to evaluate repairable technical objects. The structure of the diagnostic system was characterized, and the measurement and diagnostic subsystems were described. An artificial neural network is an important element in an intelligent diagnostic subsystem. The structure, the algorithm, the organization of a neural network and the basic relations that describe its work were presented. The information presented in the form of the vectors of diagnostic signals, and their standard vectors constitute the primary information base used in “DIAG” computer program. Training of an artificial neural network is an important aspect that is presented in the paper. The issue concerning these problems is not presented in the literature. Training of a network was presented on the grounds of teaching vectors, which are determined in a diagnostic system in the process of a simulation of a specific state in the object examined. An example of training of a network was presented in a diagnostic system which evaluates a control system of the operation of a car engine. Appropriate connections were presented for the purpose of a qualitative assessment of the training process of a neural network.

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Authors and Affiliations

  1. Department of Mechanics, Technical University of Koszalin, 15-17 Raclawicka St., 75-620, Koszalin, Poland

    Stanisław Duer & Konrad Zajkowski

  2. ATENA, ICT Financial Services, 33 Rzemielsnicza St., 81-855, Sopot, Poland

    Ireneusz Płocha

  3. OTICON Polska Production Sp. z.o.o, ul. Lubieszynska 59. Mierzyn, 72-006, Szczecin, Poland

    Radosław Duer

Authors
  1. Stanisław Duer
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  2. Konrad Zajkowski
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  3. Ireneusz Płocha
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  4. Radosław Duer
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Correspondence to Stanisław Duer.

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Duer, S., Zajkowski, K., Płocha, I. et al. Training of an artificial neural network in the diagnostic system of a technical object. Neural Comput & Applic 22, 1581–1590 (2013). https://doi.org/10.1007/s00521-012-1052-9

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  • DOI: https://doi.org/10.1007/s00521-012-1052-9

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