Design Technology and AI-Based Decision Making Model for Digital Twin Engineering

Abstract

This research considers the problem of digital twin engineering in organizational and technical systems. The theoretical and methodological basis is a fundamental scientific work in the field of digital twins engineering and applied models. We use methods of a system approach, statistical analysis, operational research and artificial intelligence. The study proposes a comprehensive technology (methodological approach) for digital twin design in order to accelerate its engineering. This technology consists of design steps, methods and models, and provides systems synthesis of digital twins for a complex system (object or process) operating under uncertainty and that is able to reconfigure in response to internal faults or environment changes and perform preventive maintenance. In the technology structure, we develop a simulation model using situational “what-if” analysis and based on fuzzy logic methods. We apply this technology to develop the digital twin prototype for a device at the creation life cycle stage in order to reduce the consequences of unpredicted and undesirable states. We study possible unforeseen problems and device faults during its further operation. The model identifies a situation as a combination of failure factors of the internal and external environment and provides an appropriate decision about actions with the device. The practical significance of the research is the developed decision support model, which is the basis for control systems to solve problems related to monitoring the current state of technical devices (instruments, equipment) and to support adequate decisions to eliminate their dysfunctions.

1. Introduction

Under the fourth industrial revolution, the driver of innovative development in high-tech enterprises is the technology of a “digital twin” (DT), or a virtual replica of a cyber-physical system, a virtual prototype of real processes or products. DTs, and more generally, computer-aided design and simulation technologies, are intended to meet the great challenge of creating complex engineering structures and technical systems that are optimal by the setting of conflicting performance criteria. The potential benefits of DT include the ability to perform what-if analyses and provide decision support by fast-verifying designs and digital tests used in changing the product, process or its components. DT applications contribute to the growth in the competitiveness of manufactured products by enhancing the market processes by reducing the time for product development, testing and implementation.

However, the organizational and methodological maintenance for DT development is still not fully developed from the point of the monitoring and coordination of physical objects. Such a methodology requires a systematic approach for an object’s design taking into account various aspects—functions performed, including the identification and solution of operational problems, complexity, purpose, life cycle stages and other object features.

The aim of this paper is to develop organizational and methodological maintenance for organizational and technical systems with a DT design, which provides a systematic synthesis of design stages, tools (methods and models) and results aimed at accelerated DT engineering. To achieve the goal, the following problems are solved:

• Generalization of approaches and methods for the modeling and designing of organizational and technical systems’ DT.
• Development of the system model and technology for setting up organizational and technical systems’ DT design process.
• Within the framework of DT model suggestions, development of the decision support model for diagnosing a device’s technical conditions and making decisions to eliminate its malfunctions based on artificial intelligence and fuzzy logic methods.

This article is organized as follows. The second chapter examines and critically summarizes methods for modeling digital twins. The advantages and disadvantages of the methods are described, and the scope of their applicability is determined. The third chapter presents the developed methodology for systems engineering and technology for the designing of organizational and technical systems’ DTs. The fourth chapter reflects a numerical example of the implementation of the developed technology and considers the digital twin of a medical device. The fifth chapter is devoted to the decision making model based on the fuzzy logic method, which is part of the digital twin. The results obtained by the model are discussed. The conclusions give the main results of the study and outline their theoretical and practical significance.

2. Literature Review

There are three groups of approaches and methods used for DT designs:

• modeling methods based on the mathematical modeling of physical processes (structural models) (Simulation-Based DT) [1,2,3,4,5];
• modeling methods based on data (Data-based DT) [6,7];
• hybrid methods (Hybrid DT) [8,9,10,11].

Features of the above methods are given in

Table 1. Comparative analysis of the models for DTs of organizational and technical system designs.

Feature	Modeling Approach
	Mathematical Modeling	Data-Based Modeling	Hybrid Modeling
Object (system) description	Describes the laws of functioning of an object (process) and its connection with the external environment. The system behavior is modeled, causal relationships and patterns are identified.	It is built on the basis of available empirical data using machine learning tools. The modeling problem is reduced to model parameters selection and some function composition	It is built on the basis of the functioning regularities and is adjusted with empirical data
Modeling principle	White box model, cause-and-effect modeling	Black box model, correlation modeling	The gray box model
Simulation and design direction	Top down	Bottom up	Top down, bottom up
Description of information certainty	Information uncertainty is controlled by input data and accuracy of modeling. Description—deterministic, stochastic	Probabilistic description of information based on data distributions in training samples	Deterministic, stochastic
Modeling methods	Numerical methods, methods of operations research, methods of simulation and situational modeling	Statistical methods, extrapolation methods, machine learning methods, big data analytics methods	Interdisciplinary models
Predictive capability	Prediction in wide ranges of parameter values described by the model	Difficulty in predicting rare events as well as in conditions of incomplete data and noisy information, as well as outside of training samples	High predictive ability within regular/ emergency situations
Priority approach to decision making and management	Decision making is based on an analysis of the overall performance (efficiency) of the system. Management decisions based on the solution of inverse problems	Decision making is based on the analysis of monitoring data and diagnostics. Management decisions are based on prediction and the solving of direct problems	Solving both direct and inverse control problems
Type of control system	Deviation control, adaptive control	Deviation control, adaptive control	Deviation control taking into account weak environmental signals; reflective control
System life cycle stage	All stages	Exploitation	Growth, stability
Operation scheme	Numerical simulation + sensors→ Data acquisition→IIoT platform	Sensors + IIoT platform→ data collection→data analytics	Mathematical Modeling + Sensors→ Data Acquisition→ IIoT-platform→analytics
Tools	Matlab Simulink, ANSYS, AnyLogic, Ithink etc.	R, Python, Statictica. GPSS etc.	Interdisciplinary Platforms

, which summarizes modeling approaches by certain characteristics.

A generalized modeling method is the mathematical modeling of the physics (functioning principles) of an object/process. The physical model provides for the computer modeling of physical processes as well as a description of their functioning and relationships with the external environment. The construction of such models in practice is associated with the mathematical programming methods (operations research) [12], and simulation modeling based on its various paradigms and approaches—system-dynamic, discrete-event or agent-based modeling [13].

Mathematical modeling is a key component of the digital transformation. To create models that are used to create system models, the DT can use the results of detailed three-dimensional numerical calculations performed using interdisciplinary CAE solvers. Depending on the purpose, mathematical models can be descriptive or optimizing. The purpose of descriptive models is to establish the laws of change in model parameters. The optimization model provides a search for the function’s extreme value under restrictions using numerical methods. Depending on the certainty of the initial information degree and taking into account the time factor, linear, non-linear, stochastic programming, game-theoretic and fuzzy logic methods can be used.

Simulation models as a subclass of mathematical models are divided into static and dynamic; deterministic and stochastic; discrete and continuous. In continuous simulation models the variables change continuously, the state of the simulated system changes as a continuous function of time, and as a rule, this change is described by systems of differential equations. In discrete simulation models, variables change discretely at certain moments in simulation time. The dynamics of discrete models are a process of transitioning from the moment of the next event to the moment of the next event.

Data-driven modeling includes data mining, artificial intelligence, big data and advanced analytic methods. Each of these methods imposes special requirements on the necessary computing resources. For example, data mining methods require large-scale storage with high bandwidth for collecting and accessing analytical data, as well as a high scalability for the computing system for processing them; machine learning methods require nodes with installed graphics accelerators.

Models based on data mining are used to discover knowledge in the data previously unknown, non-trivial, or practically useful and open to interpretation, necessary for making strategically important decisions. Artificial intelligence and machine learning are effectively used in digital warehouse forecasting. The use of these methods makes it possible to achieve a level of predictive accuracy higher than that based on traditional simulation methods [13].

The use of “big data” has its limitations, associated with incomplete or noisy data, and difficulties in predicting rare events. Extrapolation methods do not allow for such predictions. For some products, sensors are expensive to install and maintain, sensors are prone to errors, failures can give incorrect readings, and the results can overwhelm users with redundant information.

Without a structural (mathematical, physical) model, it is difficult to determine the areas of technical devices where it is advisable to locate sensors. Collecting raw data from sensors is only part of the modeling process. At the stage when the inverse problem appears, that is, when it is necessary to restore the picture of what is happening on the basis of data received from sensors without a mathematical model, this problem turns out to be intractable, since most of the collected data are unusable “garbage”, of which it is very difficult to select a meaningful part that adequately describes the object (process).

At the same time, the mathematical modeling of objects (processes) in combination with data-based models provides more opportunities for forecasting than models based only on machine learning technologies. Data-driven modeling can be applied at the operational stage of an object’s (product) life cycle, when it is possible to obtain feedback from it. Mathematical models based on physical processes are more promising for problems with a situational analysis and for decision making under “what-if?” condition analysis. In addition, hybrid models can be used in non-recurring situations where there are not enough data to apply statistical methods.

On the basis of the additional information obtained during the operation stage, the level of adequacy of the hybrid model increases, that is, the DT is trained and makes it possible to further predict the level of possible deviations from normal modes and damage to equipment, or evaluate its residual life [10].

It should be noted that at different stages of DT design, there is a different amount of data about object. At the development stage, there are no data from a real object, since there is no physical product (product) itself, and data about an object can only be obtained on the basis of modeling physical processes that determine the creation and functioning of a future product. As product data accumulate, the latter can increasingly be used to build analytical models. A mathematical model based on physical processes can be created before the stage of creating a real object, and can predict its behavior over a wide range when the boundary conditions of the numerical simulation problem change.

A DT with a high level of adequacy should combine both physical process models and data-driven models. Smart digital twins with intelligent controls should combine both of these approaches, enhancing the benefits of each of them.

The use and scope of digital twins is very wide. They are used not only in heavy engineering [14,15,16], in the automotive industry [17] and building [18], in the field of nuclear energy [19], in the aerospace [20,21] and oil and gas industries [22], in architectural design and creating smart cities [23], and agriculture, but they are also used to improve operational efficiency in the production of consumer goods [5], the accuracy of diagnostics and decision making in healthcare [24], to attract customers and customize services in the financial sector [25] and in retail [26], for organizing logistics processes and supply chains [27], and in regional and municipal management [28,29].

Demand for and the range of application of DTs is expanding. Since their development and implementation are based on a number of rapidly developing technologies, the development of digital storage directly depends on the growth of the capabilities of these technologies. This is due to:

• The development of quantum technologies and the increase in the speed of computing systems [30,31]. If general-purpose quantum computing is ever realized, there would be a qualitative leap in the speed of hardware systems. This will make it possible to perform numerical analyses based on already existing (and more complex) models in a time acceptable for the operational interaction of a physical object and its digital copy. Today, companies are working to develop and use quantum algorithms to model complex physical processes. The transition to such technologies will speed up the solution of problems based on numerical modeling, providing for the required accuracy of algorithms under the conditions of the available computing resources (problems of multi-parameter optimization, etc.);
• The development of 5G and 6G technologies [32,33,34]. These technologies have higher throughput, lower latency, and lower battery consumption of IoT sensors. This provides an increase in the speed of signal transmissions between the physical object and its DT. The use of 5G networks will make it possible to construct virtual reality services as part of digital twins and make available the virtual verification and validation of finished products.
• The development of strong artificial intelligence technology [35,36] will make it possible to build a data center in which the role of a person in making managerial decisions will be minimized. DTs will be able to provide decision making autonomously, coordinate these decisions with other DTs, and perform self-testing and diagnostics with subsequent troubleshooting. Such decision support systems based on digital data will ensure the adoption of complex decisions in aggressive and dangerous environments without the presence of a person.

3. Methodology for Systems Engineering and Technology for Digital Twin Design

Following [37], and dividing the behavior types of a real system into Predicted Desirable (PD), Predicted Undesirable (PU), Unpredicted Desirable (UD), and Unpredicted Undesirable (UU), in this work, we build a Digital Twin Prototype that describes the prototypical physical artifact. It contains the necessary components to describe and produce a physical version that twins the virtual version. We consider the “create” life cycle stage of the system (physical object). At the create life cycle stage of the system, the problem is to foresee its possible states and develop a decision support system to neutralize the consequences of unforeseen events.

While the traditional approaches have been to verify and validate the requirements, or the predicted desirable (PD), and to eliminate the problems and failures, or the predicted undesirable (PU), the Digital Twin prototype can help to identify and eliminate the unpredicted undesirable (UU) states. This problem is solved on the basis of changing the simulation parameters within the possible range, and investigates a variety of different situations; it is possible to explore the variety of behavioral patterns of the system that can lead to serious catastrophic problems. Such modeling will allow for designing the physical object in a virtual space with a number of possibilities, and will significantly reduce the consequences of UUs.

The purpose is to develop an approach for digital twin engineering of a physical object (device) that will minimize undesirable unpredictable behavior. This will mitigate or eliminate the negative consequences of such risks. To do this, we propose the following methodology.

The process of DT construction is a multi-stage process and consists of the design and engineering stage, digital modeling and technological testing (Figure 1). In this paper, we consider only the first stage of design and engineering.

DT is defined as a system consisting of a physical object digital model and two-way information links with the physical object or its components. DT is based on a digital model in the form of mathematical and computer models, as well as documents that describe the structure, functionality and behavior of a newly developed or operated product (object) at various stages of its life cycle, for which, based on the results of digital or other tests, an assessment of compliance with the requirements for the product was carried out. In this case, a digital model is created using computer simulation software and describes the structure, functions and behavior of the product being developed. The content and functionality of the digital model depends on the stage of the product’s life cycle. The conformity assessment of a digital product model generally includes verification and validation procedures for mathematical and computer models. A computer model is implemented in a computing environment and is a collection of data and program code required to work with data. The computer model is based on a mathematical model, that is, a model in which information about the modeling object is presented in a formalized form.

The organizational requirements to create a digital twin include the use of a software and technological platform for digital twins, which should include: (a) computer modeling software controls; (b) project management tools: (c) tools for collecting, processing, analyzing, visualizing, cataloging, storing, transferring computer models and computer simulation results; (d) means of tracking all changes in design, technological solutions and modifications of computer models, and options for engineering calculations; (e) means of reporting results; (f) means of data protection and organization of joint work of project participants in accordance with access rights; (g) computer simulation tools for planning the usage of an object (product) for its intended purpose; (h) maintenance and repair support.

The organizational and methodological support of DT is not fully developed in terms of the coordination of modeling and management processes including structural, functional and informational modeling. To fill this gap, we propose to form a work plan by its stages like triads: problem–content–results. These require a systems approach and design for all life cycle stages of a physical object, including the identification and solution of emerging problems in the process of its operation.

We develop the following technology, which provides organizational and methodological support for the development and operation of DT for the organizational and technical system and is presented in Figure 2. The proposed technology combines the stages of its design, methods and models, and provides for the accelerated engineering of DT.

The proposed technology allows, firstly, to carry out a system analysis of a physical object, taking into account the uncertainty of the external environment on the basis of heterogeneous tools for qualitative and quantitative analysis. Secondly, it forms an adequate mathematical model of the physical object, taking into account the results of the conceptualization stage, and develops a computer model and implements a test of it. Thirdly, it can be the basis for DT engineering and forming a decision support system.

The operational scheme of the proposed technology consists of five steps. First, it is necessary to identify the problems and describe the contradictions that arise in the development and implementation of digital twins in the industry. Next, we should determine the goals of the DT implementation, set problems based on the goal and describe the project. At the second step, the decomposition (scanning) of physical object takes place. The functions and properties as well as the technical parameters of the considered system (equipment, device) are described. Further, its structural and functional model is built. The third step is devoted to the analysis of the external environment of the functioning of the technical system. Using STEP (Social–Technological–Economic–Political) and SWOT (Strengths–Weaknesses–Opportunities–Threats) analysis, we determine important internal and external factors and expertly evaluate their impact on the effectiveness of DT. At the fourth step, mathematical and computer modeling tools and methods for DT designing are chosen. In addition, a decision support system is being built based on the selected mathematical model, and simulation experiments are being carried out.

4. Empirical Results

As an example of the implementation of the proposed technical device at the creation life cycle stage—a neonatal intensive care incubator with microprocessor controls for monitoring the parameters of temperature, oxygen concentration, air humidity, temperature and body weight is considered. Application of the technology is described by the steps defined in Figure 2.

Step 1. The incubator is designed for the nursing and intensive care of newborns, including premature babies with critically low weight (from 500 g). The incubator provides an adjustable heat supply, the required air humidity and oxygen concentration in the children’s module, and body weight control.

Step 2. First, we form the structural and functional models of the device. The block diagram is shown in Figure 3 and consists of a sensor system, control system, temperature control system and oxygen supply. The observed parameters of the device are: (a) air temperature; (b) skin temperature; (c) relative air humidity; (d) oxygen concentration; (e) body weight.

The construction of a functional model allows for clearly fixing what processes are carried out—what information objects are used when performing functions of various levels of detail. The model shows the areas of responsibility of the process executors and the course of the process itself, the relationships between processes and the results. The functional model is the basis for identifying problems and weaknesses in the operation of the device.

The functional model of the incubator is formed on the basis of the notation of system modeling of business process IDEF0. Figure 4 shows the first level of decomposition and reflects the main functions.

Step 3. At the next step, the diagram of cause-and-effect factor relationships is formed to identify possible causes of failures. Based on a qualitative analysis of similar devices of this class [38], it can be assumed that the following six factors are the main sources of incubator malfunction: (1) medical personnel; (2) technical staff; (3) external environment; (4) sensor system; (5) control system; (6) power system. The diagram shows the decomposition of these factors in the form of problems that, acting in isolation or together, can lead to incubator failure (Figure 5).

Decision Support Model for Diagnosing the Technical Condition of Equipment Based on Fuzzy Logic Methods

Steps 4, 5. Identified problems and possible causes of equipment failure show that a number of factors and causes are described not in quantitative, but in qualitative form. Therefore, a decision support system should use possibilities to use different measurement scales of the simulated device properties. Problems of this type can be solved using artificial intelligence methods and fuzzy logic models [39,40,41].

The category of technical condition depends on many factors, both qualitative and quantitative. The developed model considers fault factors that are related to the internal technical condition of the device itself, that is, faults associated with the baby module engine, power cable, oxygen connection system, valve impeller system, humidity sensor, temperature sensor of the main and additional, etc.

The implementation of the fuzzy modeling process is carried out by using the Fuzzy Logic Toolbox module of the MATLAB software tool. Fuzzy inference is implemented based on the Mamdani algorithm.

To diagnose the technical condition of a device, its qualitative description is made using linguistic expressions (logical rules). Twenty-five input variables are used, reflecting the state of the equipment subsystems, determining one of the four possible decisions on the action with the equipment (output variable)—to take out of service, to repair, to conduct additional preparation for operation, to keep in service (

Table 2. Description of input and output variables.

Indicator	Description	Values
Input variables
I1	Adjusting height position of the baby unit	Impossible, possible
I2	Adjusting oblique position of the baby unit	Impossible, possible
I3	Baby module engine	Fault, overheat, serviceable
I4	Baby unit switch mechanism	Fault, correct
I5	Position height mechanism of the baby module	Turns off, does not turn off
I6	Air temperature under the hood	Low, normal, high
I7	Filter	Dirty, clean
I8	Filter installation time	More than three months, less than three months
I9	Water in the tank of the humidifying system	Absent, present
I10	Valve system	Dirty, clean
I11	Indicator	Red, network, flicker
I12	Power cable	Not attached, attached
I13	Sound signal	3, 2, intermittent, continuous
I14	Fan impeller	Installed wrong, installed correctly
I15	Fan	Faulty, correct
I16	Display humidity sensor	Faulty, correct
I17	Regulating humidity sensor	Faulty, correct
I18	Main skin temperature sensor	Fault, serviceable, not connected, connected
I19	Secondary skin temperature sensor	Fault, serviceable, not connected, connected
I20	Air oxygen concentration	Low, medium, high
I21	Oxygen connection system	Fault, correct
I22	Control system	Fault, good
I23	Obsolescence	Absent, present
I24	Physical deterioration	Not removable, removable
I25	Fusible link	Burnt out, not burned out
Output variable
O1	Technical condition and operation with device	To take out of service, to repair, to conduct additional preparation for operation, to keep in service

).

Linguistic variables and the range of their possible values are described in

Table 3. Description of linguistic variables.

Indicator	Qualitative Meaning	Range of Linguistic Values
I1, I2, I4, I5, I7, I8, I9, I10, I12, I14, I15, I16, I17, I21, I22, I23, I25	“Impossible”, “Faulty”, “Not turn off”, “Dirty”, “More than 3 months old”, “Missing”, “Not connected”, “Installed incorrectly”, “Cannot be repaired”	(0; 0.35; 0.7)
I1, I2, I4, I5, I7, I8, I9, I10, I12, I14, I15, I16, I17, I21, I22, I23, I25	“Possible”, “Serviceable”, “Disconnecting”, “Clean”, “Less than 3 Months”, “Present”, “Attached”, “Installed Properly”, “Retiring”	(0.4; 0.7; 1)
I3, I6, I11, I20.	“Low”, “Red”, “Low”, “Fault”	(0; 0.2; 0.4)
I3, I6, I11, I20	“Normal”, “Network”, “Medium”, “Overheat”	(0.3; 0.5; 0.7)
I3, I6, I11, I20	“Increased”, “High”, “Central alarm”, “Serviceability”	(0.6; 0.8; 1)
I13, I18, I19, O1	“Take it out of service”, “Not operating”	(0; 0.175; 0.35)
I13, I18, I19, O1	“Repair”, “Serviceable”	(0.2; 0.375; 0.55)
I13, I18, I19, O1	“Intermittent”, “Perform additional preparation for operation”, “Not connected”	(0.4; 0.575; 0.75)
I13, I18, I19, O1	“Continuous”, “Keep in service”, “Connected”,	(0.65; 0.825; 1)

.

The accumulation of the conclusion according to all the rules is carried out using the operation of max-disjunction. The center of gravity method is used for defuzzification. By implementing a fuzzy inference system at the defuzzification stage, we obtain a decision about operation modes under conditions of known input data.

The rule base for making decisions on the technical condition and action with the device in the event of a malfunction is determined using a set of production rules of the “If-Then” type (selectively):

• If (I₁ is “possible”) and (I₂ is “possible”) and (I₃ is “serviceable”) and (I₄ is “faulty”) and (I₅ is “not turn off”) then (O₁ is “to repair”);
• If (I₁ is “possible”) and (I₂ is “possible”) and (I₃ is “serviceable”) and (I₄ is “serviceable”) and (I₅ is “not turn off”) then (O₁ is “to keep in service”);
• If (I₇ is “clean) and (I₉ is “present”) then (O₁ is “to keep in service”);
• If (I₁₁ is “red”) and (I₁₃ is “2”) and (I₂₀ is “low”) and (I₂₁ is “serviceable”) then (O₁ is “to repair”);
• If (I₂₀ is “average”) and (I₂₁ is “serviceable”) then (O₁ is “to keep in service”);
• If (I₁₁ is “central alarm”) and (I₁₃ is “3”) and (I₂₀ is “medium”) and (I₂₂ is “faulty”) then (O₁ is “to repair”);
• If (I₂₃ is “present”) and (I₂₄ is “non removable”) then (O₁ is “to take out of service”);
• If (I₂₃ is “missing”) and (I₂₄ is “non recoverable”) then (O₁ is “to take out of service”);
• If (I₁ is “not possible”) and (I₂ is “possible”) and (I₃ is “overheating”) and (I₄ is “correct”) and (I₅ is “shutdown”) then (O₁ is “to conduct additional preparation for operation”).

5. Discussion of Results

Using the developed technology and model, we conduct the situational “what-if” analysis, and identify a device’s technical conditions and its possible faults. The model delivers decisions in various situations, determined by a combination of input variables. We consider multivariate situations with different input factor combinations. The following types of situations are tested: (1) light network indicator is on, continuous signal sounds; (2) red alarm indicator flashes, signal “3” sounds; (3) baby module does not function. Results of the situational analysis are presented in

Table 4. Modeling results on Situation 1.

Situation 1: If Indicator is “Network” and Sound Signal is “Continuous” and…	Input Variables					Output Variable O1
	I11	I13	I12	I25	Others
1-1. Power cable is “not connected”	0.5	0.8	0.2	0.7	0.7	0.59
1-2. Power cable is “connected”	0.5	0.8	0.8	0.8	0.7	0.92
1-3. Fusible link is “burnt out”	0.5	0.8	0.1	0.35	0.7	0.376
1-4. Fusible link is “not burnt out”	0.5	0.8	0.9	0.2	0.7	0.95

,

Table 5. Modeling results on Situation 2.

Situation 2: If Indicator is “Flicker” and Sound Signal is “3” and…	Input Variables					Output Variable O1
	I11	I13	I12	I25	Others
2-1. Main skin temperature sensor is “not connected”, secondary skin temperature sensor is “connected”	0.1	0.3	0.5	0.8	0.6	0.74
2-2. Main skin temperature sensor is “connected”, secondary skin temperature sensor is “not connected”	0.1	0.3	0.9	0.6	0.6	0.65
2-3. Main skin temperature sensor is “not connected”, secondary skin temperature sensor is “not connected”	0.1	0.3	0.7	0.55	0.6	0.574
2-4. Main skin temperature sensor is “fault”, secondary skin temperature sensor is “fault”	0.1	0.3	0.1	0.2	0.6	0.454
2-5. Main skin temperature sensor is “serviceable”, secondary skin temperature sensor is “fault”	0.1	0.3	0.35	0.1	0.6	0.89
2-6. Main skin temperature sensor is “fault”, secondary skin temperature sensor is “serviceable”	0.1	0.3	0.3	0.4	0.6	0.95

and

Table 6. Modeling results on Situation 3.

Situation 3: Baby Module Does not Function	Input Variables						Output Variable O1
	I1	I2	I3	I4	I5	Others
3-1. Position height mechanism of the baby module is “not turn off”, baby unit switch mechanism is “fault”, baby module engine is “serviceable”	1	1	0.7	0.2	0.4	0.5	0.464
3-2. Position height mechanism of the baby module is “not turn off”, baby unit switch mechanism is “correct”, baby module engine is “fault”	1	1	0.1	0.7	0.3	0.5	0.52
3-3. Position height mechanism of the baby module is “not turn off”, baby unit switch mechanism is “fault”, baby module engine is “fault”	1	1	0.3	0.5	0.2	0.5	0.34
3-4. Adjusting the height position of the baby unit is “impossible”, baby module engine is “overheat”	0.25	1	0.5	0.9	1	0.5	0.62
3-5. Adjusting the height position of the baby unit is “impossible”, baby module engine is “fault”	0.3	1	0.1	0.9	1	0.5	0.376
3-6. Adjusting the height position of the baby unit is “impossible”, baby module engine is “serviceable”	0.1	1	0.8	0.9	1	0.5	0.75
3-7. Adjusting oblique position of the baby unit is “impossible”, baby module engine is “fault”	1	0.5	0.2	1	0.8	0.5	0.45
3-8. Adjusting oblique position of the baby unit is “impossible”, baby module engine is “overheat”	1	0.2	0.6	1	0.8	0.5	0.68
3-9. Adjusting oblique position of the baby unit is “impossible”, baby module engine is “serviceable”	1	0.6	0.9	1	0.8	0.5	0.7

.

In the situation when the indicator is “network” and the sound signal is “continuous”, a fusible link is “burnt out”, and the appropriate decision made by model is “to repair”. In the situation when the indicator is “flicker”, the sound signal is “3” and the main skin temperature sensor is “connected”, the secondary skin temperature sensor is “not connected”, the decision is “to conduct additional preparation for operation”. If the position height mechanism of the baby module is “not turn off”, the baby unit switch mechanism is “fault”, and the baby module engine is “fault”, then the decision is “to take out of service”.

The developed decision making model for eliminating device malfunctions as a part of its DT takes into account internal and external factors and allow for identifying the malfunction problem and suggesting the appropriate decision to provide a regular mode of device operation. In the framework of decision support systems of DT, the model provides a reduction in device downtime, reduced repair costs and improved operational efficiency.

6. Conclusions

The focus of this paper is the complex process of DT construction. In this study, we have given a comprehensive analysis of approaches and methods for organizational and technical systems’ DT design. DT construction requires multi-stage technology, and consists of design and engineering stage, digital modeling and technological testing. In this paper, we consider the design and engineering stage.

The design and engineering stage is quite time consuming and includes steps to study object properties and its connections with the external environment, describe its structure and functioning, and identify possible problems in the process of functioning. In order to organize the work at this stage and describe the operation of the device, its structure and possible failures in the operation, this study proposes an approach to organizing the design process. As a numerical example, we use the device at the design stage of its life cycle; we study possible unforeseen problems during its further operation. Possible combinations of failure factors of the internal and external environment are modeled, and the model based on fuzzy rules is proposed for making management decisions in such situations.

It is shown that for complex organizational and technical systems functioning under uncertainty, there is no comprehensive and universal methodological approach for organizing a DT design and its accelerated engineering. We consider the digital twin prototype for a device in the creation life cycle in order to reduce the number and consequences of unpredicted undesirable states. The new theoretical results have been obtained over investigation:

• The technology for organizing systems’ DT design has been proposed. The technology differs from others in that it combines design stages, methods and models, and provides DT accelerated engineering.
• The decision support model for diagnosing the technical condition of a technical device has been developed. The model is based on methods of situational analysis and fuzzy logic, and provides decision making under miscellaneous internal and external factors having a quantitative or qualitative nature. The model increases the accuracy and reliability of a decision support system and provides a synthesis of effective decisions in various situations and combinations of heterogeneous factors. Using observations of the object state, the model identifies, responds to changes and provides a basis for making decisions about future actions.

The practical importance of the developed technology and the model is that they are the foundation for decision support systems to observe the current state of technical devices (instruments, equipment) and to develop adequate decisions to eliminate its malfunctions.