SNNKIQ IMPROVING DIESEL ENGINE DIAGNOSTICS RELIABILITY AT UNSTEADY LOAD

Abstract

The issues of rational use of energy in the operation of machine-tractor units by reducing downtime, including hidden ones, are very relevant. An increase in the energy efficiency of machine-tractor units can be achieved by diagnosing diesel engines according to functional parameters that are used as the power plant of unit. The difference in engine loading parameters during diagnosis and operation can be taken into account by correction factors for the tolerance of diagnostic parameters, which are determined for each brand of engine. (Research purpose) The research purpose is developing the methodology for determining correction coefficients for the tolerances of diagnostic parameters, taking into account the distribution of input effects. (Materials and methods) The values of the functional parameters of tractors in real operation significantly differ from the indicators in bench conditions. The probabilistic, functional transformation of random variables and computational methods were applied. The article proposes calculating the mathematical expectations of the functional parameters of the engines using the functional transformation of random variables and probabilistic characteristics of the input effect to simplify the process. (Results and discussion) Without taking into account the correction coefficients, the error in determining the deviations of functional parameters can be significant. Deviations of functional parameters from the values of the typical characteristic are maximal for the arcsin function and minimal for the Gauss law. The methodology for determining the correction coefficients for the tolerances of functional parameters was substantiated using the example of the D-240 engine. (Conclusions) The article presents quantitative characteristics of correction coefficients for the transition from operational tolerance to diagnostic. For most technological operations, diagnosing diesel engines with an unsteady load will reduce the error in monitoring the permissible values of functional parameters by 10-24 percent.