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Generation of Production Rules with Belief Functions to Train Fuzzy Neural Network in Diagnostic System

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Abstract

The article examines some algorithms for joint processing of raw data on the state of a complex multistage continuous production process to obtain probabilistic characteristics of abnormal critical events that can potentially lead to single failures or even emergencies. The article, thus, proposes and substantiates an approach to developing a technology to detect and predict malfunctions and determine their causes. The sequence of operations to process and convert diagnostic process data is considered essential. As a result, the article presents a general diagnostic model of a multistage production process. The model can formalize the main objects and processes in terms of the problem being solved. An incident is defined as an abnormal critical event described by non-normative values of diagnostic variables. Incidents are shown to be indicated by the corresponding membership functions. The hypotheses on potential incident causes are discussed to be built with belief functions being the basis of evidence theory or Dempster–Shafer theory. The hypotheses are characterized by an interval of malfunction probability in some process chain. The authors propose a procedure of converting these hypotheses into fuzzy production rules automatically. The automatical procedure is a prerequisite to using fuzzy neural networks to obtain a reliable estimate of the degree of belief in the incident cause. As a summary, the generated database of the production rules to train a neural network is substantiated to be used with the TSK architecture that makes possible to estimate a malfunction probability in the process chain quickly without resource-intensive computations.

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Funding

This work was done at the Tver State Technical University with supporting of the Russian Foundation of Basic Research (project no. 20-07-00199) and at the JSCC RAS as part of the government assignment (topic FNEF-2022-0016). Supercomputer MVS-10P was used in research.

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Authors and Affiliations

  1. Tver State Technical University, 170026, Tver, Russia

    V. K. Ivanov & B. V. Palyukh

  2. Joint Supercomputer Centre of the Russian Academy of Sciences, 119991, Moscow, Russia

    A. N. Sotnikov

Authors
  1. V. K. Ivanov
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  2. B. V. Palyukh
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  3. A. N. Sotnikov
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Corresponding authors

Correspondence to V. K. Ivanov, B. V. Palyukh or A. N. Sotnikov.

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(Submitted by A. M. Elizarov)

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Ivanov, V.K., Palyukh, B.V. & Sotnikov, A.N. Generation of Production Rules with Belief Functions to Train Fuzzy Neural Network in Diagnostic System. Lobachevskii J Math 43, 2853–2862 (2022). https://doi.org/10.1134/S1995080222130169

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  • DOI: https://doi.org/10.1134/S1995080222130169

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