Multi-factor model of diagnostic signals parameters vector formation for run on variables loading-speed modes machines

Abstract

The main and most important direction in the strategy for improving the operational reliability of the dynamic equipment mining, processing industries, production and transport industry is meeting the challenge of timely detection of and localization defects at an early stage of their development. This approach ensures the improvement of technology maintenance and repair, reduce operating costs and improve availability factor. To implement measures to improve reliability and safety, dynamic equipment at the facilities of the petrochemical production and transport complexes has been equipped with various condition monitoring systems for more than twenty years. The amount of information received by systems is usually great. However, the information quality is more important than its volume for accurate and timely recognition of error conditions and monitoring the development of faults in time. Therefore, an important task is to establish the diagnostic signs informative, determine their relationship with the relevant faults classes, as well as to establish the pattern of diagnostic signs changes in time in order to predict the moment of transition of nodes to the limit state. The model of the diagnostic signal parameters vector formation is presented in this paper. This model takes into account the influence of concomitant factors on some parameters values. These parameters values depend both on the object state as well as on a number of concomitant processes and their parameters. In terms of the problem, the concomitant factors can be divided into two groups: controlled and uncontrolled. The controlled factors measurement can be carried out in parallel with the diagnostic signal parameters measurement. The uncontrolled factors are parameters that are difficult or impossible to measure. The uncontrolled factors include all kinds of environmental fluctuations. The theoretical relationship between the technical condition and the diagnostic signal parameters, taking into account the influence of concomitant factors is described by presented diagnostic model.