Development of the method for individual forecasting of technical state of logging machines

Abstract

The appearance of reliability engineering is connected with the study of functional dependencies and quantitative relationships between failures and their causes, as well as the development of calculation methods. Processing of statistical materials in the field of reliability requires the development of existing statistical methods and leads to the rise of statistical characteristics of reliability and the patterns of failures, which serve as the basic formation of the statistical reliability theory. The generalization of statistical materials on failures and the development of recommendations for improving the reliability of products cause the determination of mathematical laws governing the failures, as well as the development of methods for measuring reliability and engineering calculations of its indicators. The study of the physical causes of failures, patterns of aging and strength of materials, the influence of various external and internal effects on the performance of products, is the subject of the physical reliability theory. The main cause of failures of logging machines is various processes of destruction, leading to irreversible changes in structural elements. These changes are caused by wear, accumulation of deformation and fatigue, corrosion, diffusion of one material into another, etc. In one logging machine and even in the same structural element, these processes are superimposed, they interact with each other, and ultimately cause a change in the parameters characterizing the technical condition. It can be concluded that, at present, as a rule, information about changes in the technical parameters of machines in operation is not being recorded. As a result, it is not possible to determine the initial vector process. Therefore, at the first stage, it is supposed to determine it by statistical modeling and estimate the forecast coefficients. Then, using the information received from maintenance and repair for each machine, specify the forecast factors, i.e. solve the problem of individual forecasting in an adaptive way.