Obtaining Reliability Models of Technical Objects From Potential-Streaming Equations of Physical and Chemical Processes in These Objects

Distributed computing system for creating digital portraits of complex systems

“…Having obtained a model of the considered system by converting the processes dynamics equations system (a block diagram of the system), and having the test statistics of the system various instances (statistics of the control parameters of the system instances), we test the resulting model [19]. If such a model has been tested, then the processes in the considered system, as well as the state functions for the properties of substances and processes [7,8], were set correctly [9], otherwise, it is necessary to consider other options for the processes and state functions for the properties of substances and processes [7,8,9].…”

“…Then, in the general case, we construct a probabilistic model of this system that can be used to solve various practical problems [19]. For this purpose, the resulting model is used to calculate the statistics of the values returned by this model (parallelization is performed for subsets of the values of the available statistics), as well as, if necessary, all the necessary statistical characteristics [19]. Then, for these obtained values, a model (in general, probabilistic) is constructed [19] (parallelization is similar to the one described above) -a digital portrait of the considered system [22].…”

“…In order to use this system of equations to solve practical problems of system development (synthesis of optimal structure and selection of optimal parameters [11 -14]) in accordance with the specified requirements [11,12], as well as the tasks of operating these systems (synthesis of the control system [15], diagnostics and forecasting of the technical condition [12,16], analysis of the system reliability [12,16,17], development of the system maintenance methodology [17]), it is necessary to transform the resulting system of equations of process dynamics in such a way that only the observed and controlled parameters remain in the resulting system of equations [9,10]. As a result of this transformation (in general, by numerical and analytical methods [10,[18][19][20], which consists in calculating the dynamics of the observed and controlled parameters of the system under study for various randomly set parameters of the processes dynamics in this system and further approximating the model of the system under study on a set of the observed and controlled parameters dynamics [10,18,19]), the relationship between the controlled parameters of the system and the observed parameters is obtained with accuracy to the control parameters (obtained from the test results of the considered system and laboratory systems) [10]. The controlled parameters of the system also include the observed parameters values predicted for subsequent time moments [10].…”

“…Based on the control parameters statistics from the considered system obtained model, the conditional probabilistic characteristics of the controlled parameters are determined [19] (using the methods of probability theory [21]), for which the model [19] is constructed, which is directly used to solve the practical problems described above. The resulting mathematical model, which connects the controlled parameters with the observed ones, is a digital portrait of the considered system [22], which can be used, in particular, for diagnosing and predicting the state of electricity consumers from the intelligent distribution system [22 -26], as well as electricity sources [22].…”

“…As can be seen from the above, the formation of a system model is quite a time-consuming process [9,10,18,19]. This leads to the need for software implementation of the presented methodology for creating a digital portrait, which includes multiprocessor and multicomputer parallelization [9,19,27].…”