The study considers the application of accelerated life testing (ALT) to assess the reliability of agricultural machinery components subjected to intensive operational loads. The relevance of the study is due to the need to reduce the testing time and reduce the cost of machinery maintenance, especially in the context of increasing food security requirements. A complex methodology including the impact of salt fog, climatic factors and ultraviolet radiation for modeling of aggressive operating conditions is proposed. Special attention is paid to “minor” components (hydraulic systems, electronic modules, fasteners), which account for up to 40% of equipment failures. On the basis of the theory of potential efficiency the coefficients of acceleration of degradation processes are calculated, which allows to reduce the testing time by 4–7 times while maintaining high accuracy of resource prediction (85–90%). The results of the study demonstrate the possibility of optimizing design solutions and reducing operating costs in the agro-industrial complex.

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Assessment of Efficiency of Accelerated Resource Testing of Agricultural Machinery Components

  • N. V. Limarenko,
  • D. N. Savenkov,
  • A. A. Shcherbakov,
  • D. I. Gladckih

摘要

The study considers the application of accelerated life testing (ALT) to assess the reliability of agricultural machinery components subjected to intensive operational loads. The relevance of the study is due to the need to reduce the testing time and reduce the cost of machinery maintenance, especially in the context of increasing food security requirements. A complex methodology including the impact of salt fog, climatic factors and ultraviolet radiation for modeling of aggressive operating conditions is proposed. Special attention is paid to “minor” components (hydraulic systems, electronic modules, fasteners), which account for up to 40% of equipment failures. On the basis of the theory of potential efficiency the coefficients of acceleration of degradation processes are calculated, which allows to reduce the testing time by 4–7 times while maintaining high accuracy of resource prediction (85–90%). The results of the study demonstrate the possibility of optimizing design solutions and reducing operating costs in the agro-industrial complex.