The problem of system design of complex technical objects based on intelligent technologies is considered. An optimization model for the conceptual design of a micro-minisatellite based on a genetic algorithm is discussed. An artificial neural network model of a propulsion system is considered, as well as a heuristic algorithm for analyzing the crosscorrelation of telemetric parameters of a micro-mini-satellite. The concept of constructing an intelligent system for information support of the life cycle of a complex technical object based on the considered models and algorithms is proposed.
1. Introduction One of the directions for improving the life cycle management processes of complex technical objects is the transition to contracts for the full product life cycle. An objective need in recent years has been the optimization of the relationship between the customer and the manufacturer, focused on supporting the operation of complex technical object, increasing the degree of readiness and achieving the specified tactical and technical characteristics. This approach is implemented in the form of the concept of Performance-Based Lifecycle Product Support (CoPBL) [1, 2]. According to this approach, the manufacturer receives comprehensive access to product life cycle information during the operation stage.
economic efficiency can be based on the intelligent information support with using the integrated life cycle models. The construction of end-to-end models of the object life cycle becomes possible due to big data processing technologies that integrate distributed data and make it possible to increase the efficiency of using the above-mentioned components of the information support system [11-13].
estimating the remaining fuel supply and technical condition under conditions of uncertainty,
The process of accumulation of information at the pre-operational stages of the micro-mini-satellite life cycle is shown in figure 2.
To support decision-making during satellite operation, additional information is required on the previous life cycle stages, i.e. design, manufacturing, and testing. Data can be located in distributed storages and relate to the area of responsibility of various organizations that are participants in the life cycle.
At various stages of the micro-mini-satellite life cycle, it becomes necessary to process and store large amounts of information, which can be further used to increase the efficiency of flight control and perform objective operations. In figure 2 three levels are presented: level of life cycle processes, level of data generation, data transition and storage in local databases; level of aggregation of entire data into an Aggregate Database.
necessary to create many alternative versions of the product V j , j = 1, J , contained in the product database of the product data management system (PDM).
Basic characteristics of design optimization are various constraints gi , i = 1, I of mass, dimensions, cost, power consumption, reliability, accuracy and others. These constraints are represented as the required ranges of values for operation units of the corresponding level Gireq of system architecture.
functions Ui , i = 1, IU . Variable parameters are the characteristics of the lower level elements X = {x1,, xm , xM } . The architecture selection is based on the assessment of alternatives according to a variety of criteria K1,, K N
→ max that determine the objective functioning of the micro-mini-satellite.
The task of optimizing the design parameters of a complex technical object on a set of constraints is an NP-complex mixed integer multicriteria nonlinear programming problem. To solve such tasks, it is possible to use optimization genetic algorithms. In terms of the genetic algorithm, the micro-minisatellite version is an individual; the components and subsystems of the product (for example, orientation and stabilization system, propulsion system and others) are chromosomes; the characteristics and attributes of product components are genes. The result of the genetic operators of crossing and mutation are new products that are individuals with improved characteristics in accordance with given constraints.
The characteristics or genes of the lower level can be numerical, interval or multiple values. The groups of such characteristics are denoted, respectively, by Xn, Xi, and X m. Set of genes or characteristics is denoted by
X = X n X i X m = {x1,, xm , xM }. Then, by nonlinear convolution, the multicriteria problem
K1,K N → max reduces to a single criterion:
F (K1,K N ) → extr
gi (x1, , xM ) Gireq , i = 1, I Ui , i = 1, IU ,
X = X n X i X m = {x1, , xm , xM }
(
complex technical object and choose the optimum for a given set of criteria. When forming the system architecture of a technical object, a large amount of reliable information is required. It is not always possible to measure a number of important parameters in the system, for example, fuel consumption.
operational data stream, we can apply a model based on a complex of artificial neural networks and a logic unit. The developed artificial neural network model includes three neural networks ANN1,
method of back propagation of error. As a training sample, we used the calculated and experimental data obtained at the design and testing stages of the micro propulsion systems. The input X is fed to the propulsion systems for the obtaining of thrust, and the output Y allows to evaluate the amount of thrust.
of the output information Y* on the input X and is used in the simulation mode of the operation of the micro-mini-satellite remote control.
determines the dependence of the input information X* on the output Y. The results of the operation of the ANN2 neural network are used in the monitoring mode to solve the problem of diagnosing the technical condition of the micro-mini-satellite propulsion system.
X Y ANN1 Y* … X*
… ANN2 ANN3
logic block provides the interaction of ANN1 and ANN3. In the monitoring mode interaction of ANN2 and ANN3 also improves the accuracy of estimating fuel consumption Gτ using additional information about the technical condition Sτ of the propulsion system.
5. Heuristic algorithm for analyzing the cross-correlation of telemetry parameters of a micromini-satellite
information is accumulated. On the base of this information, in the conditions of the mission control center operation, it is possible to obtain a forecast of the remaining life of the micro-mini-satellite.
functional relationships between the technical parameters of the micro-mini-satellite subsystems, which can be complexly correlated. The analysis of the relationships between the telemetric parameters of the micro-mini-satellite is a complex scientific and technical task, which in the conditions of a large flow of information can be solved by developing intelligent algorithms for analytical data processing.
to use the analysis of the cross-correlation of telemetric parameters of the main subsystems of the micro-mini-satellite. The reason of the cross-correlation of telemetric parameters is determined by the design features, the mechanisms of interaction of the micro-mini-satellite subsystems and the architecture of the on-board system.
The regression analysis of the telemetric data set X = {x1(t), xi (t), x j (t), xN (t)} , where
xi (t) is the pulse discrete signal of i-th telemetric parameter, is difficult due to its large dimension, as
well as the diversity and complexity of the relationship between telemetric parameters. In addition, a
number of telemetry parameters have time delay relative to each other with a random time lag
(crosscorrelation radius). Thus, the direct application of regression and correlation analysis tools is difficult.
Therefore, instead of signals xi (t ) it is proposed to use peak-discrete function ~xi (t ) :
sign xi (tk−1) , if xi (tk−1) xi (tk ) 0, k = 0,1,
xi (t) = t t t
0, otherwise
(
difference in the shape of the signals xi (t ) and x j (t) , gives an unsatisfactory result, the crosscorrelation function of peak-discrete signals ~xi (t ) and ~x j (t) :
Bi, j () = 1 K
~xi (t)~xj (t − ) .
K t=0
(
An analysis of the dynamics of the correlation function Bi, j () together with an estimate of the expected value M i, j () and variance i2, j () of the cross-correlation of signals gives a developed representation of the cross correlation between the telemetry parameters i and j.
telemetry data of the micro-mini-satellite are proposed, which performs the following functions: • detection of cross-correlation between tele metric parameters; • determination of the type of correlation, namely instant or with a random time lag; • analysis and control of statistical indicators of cross-correlation Bi, j () , M i, j () , and i2, j () .
subsystems: information collection and processing subsystem; subsystem of three-dimensional modeling and visualization; subsystem for assessing the resources and state of the micro-mini-satellite; a complex of integrated life cycle models of a complex technical object.
territorially remote technical experts and users of aerospace information in a distributed mode.
computer models has been created, including 3D models of the micro-mini-satellite design and its control systems, information models of the production process, virtual 3D models of satellite operation processes, models of logistics supply chains in the interaction of designers, manufacturers, micromini-satellite operators and consumers of objective information.
measurements of a large number of parameters over long time intervals. The volume of telemetric information can be significant, and the rate of data feed into the aggregated database is relatively high.
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