Preface

**Section 3 Modeling of Natural Hazards 123**

**VI** Contents

Chapter 6 **Natural Hazards: Systematic Assessment of Their Contribution**

Alexey Markov, Alexander Barabanov and Valentin Tsirlov

Vladimir Chebotarev, Boris Davydov and Kseniya Kablukova

Alexey Markov, Alexander Barabanov and Valentin Tsirlov

**Information-Psychological Conditions on the Level of Systems**

Chapter 11 **Probabilistic Analysis of the Influence of Staff Qualification and**

Igor Goncharov, Nikita Goncharov, Pavel Parinov, Sergey

**Preventive Measures for Space Systems and Technologies 195**

Chapter 9 **The Approach of Probabilistic Risk Analysis and Rationale of**

**to Risk and Their Consequences 125** Berg Heinz-Peter and Roewekamp Marina

Chapter 7 **Models for Testing Modifiable Systems 147**

**Train Flow 171**

Nikolay Paramonov

**Section 6 Modeling for Information Security 211**

**Information Systems 213**

**Information Security 233**

Kochedykov and Alexander Dushkin

**Countering Terrorist Threats 257**

Chapter 12 **Analysis of Terrorist Attack Scenarios and Measures for**

Dmitry O. Reznikov, Nikolay A. Makhutov and Rasim S.

**Section 7 Modeling for Systems Protection Against Terrorist Threats 255**

Akhmetkhanov

Chapter 10 **Periodic Monitoring and Recovery of Resources in**

**Section 5 Modeling of Transport and Cosmic Systems 169**

Chapter 8 **Probabilistic Model of Delay Propagation along the**

**Section 4 Modeling of Automotive Equipment and Systems 145**

One can't embrace the unembraceable *Kozma Prutkov, 1854*

Truth is what stands the test of experience… The significant problems we have can't be solved at the same level of thinking with which we created them *Albert Einstein, 1879–1955*

Today there are always situations when results of tests and experience cannot solve system engineering problems "at the same level of thinking with which we created them." These tests and our experience may be incapable of predicting the "truth." This is a consequence of high complexity and uncertainty. In these conditions, probabilistic models are quite often applied to predict and estimate defined results.

In this book, various sets of original and traditional models applicable to different systems are presented. The content is structured in sections: General Propositions for Solving Ana‐ lytical Problems (two chapters), Modeling of Industrial Systems (three chapters), Modeling of Natural Hazards (one chapter), Modeling of Automotive Equipment and Systems (one chapter), Modeling of Transport and Cosmic Systems (two chapters), Modeling for Informa‐ tion Security (two chapters), and Modeling for Systems Protection Against Terrorist Threats (one chapter). This means that the application area of the presented models is wide enough, and dozens of practical examples confirm achievable effects. Certainly, the illustrated practi‐ cal possibilities of probabilistic modeling cannot cover the huge set of problems in system engineering. Nevertheless, in searching for the "truth" the presented chapters estimate the wonderful possibilities of probabilistic modeling from different points of view.

The purposes of this text are to enrich your knowledge of probabilistic modeling and to ex‐ pand the application borders for solving modern system engineering problems.

Two basic ideas define the concept of this book.

The first idea for reader to understand is the time of innovations in probabilistic modeling, and not to be late with their implementations at levels of system engineering. Today, system engineering is an interdisciplinary approach governing the total technical and managerial effort required for transforming a set of stakeholder needs, expectations, and constraints in‐ to a solution and to support that solution throughout its life. Therefore, each engineer should know about the possibilities of probabilistic models for researching system operation in changing conditions and threats. For the wary reader, who expects the proposed ap‐ proaches "to embrace the unembraceable" under the pretext of the coming globalization, we can say "Do not worry—you are not late yet." However, please do not hesitate to pay atten‐ tion to probabilistic modeling. Many specialists refused to believe Kozma Prutkov's aphor‐ ism "One can't embrace the unembraceable, 1854," but they do already actively implement "an embrace of the unembraceable" according to international standards requirements for system engineering. The scope of these standards covers different systems (system is de‐ fined as a combination of interacting elements organized to achieve one or more stated pur‐ poses, ISO/IEC/IEEE 15288). And there are no limitations—indeed it is the age of innovations! It seems that the systems known to the reader can be covered by this definition of "system." Not simply the main "dishes," proposed by the authors of the book in the form of probabilistic models, but also many "garnishes" in the form of detailed examples of their applications can be interpreted as innovative views.

analysis and mathematical modeling on specializations, e.g., "system engineering," "opera‐ tions research," "enterprise management," "project management," "risk management," "qual‐

The digital world, the Internet, and the 4th industrial revolution are changing the modern systems (planned, creating, or used) and resulting in different conditions of uncertainty in their life cycles, including operation. The changes and our growing knowledge about sys‐ tems and conditions during life cycles generate many challenges and problems concerning quantitative analysis and optimization. To meet these challenges adequately and to solve problems preventively, the probabilistic modeling in system engineering is widely used. This book demonstrates the original probabilistic ways to solve different problems by ana‐ lyzing risks for the given prognostic period in the future. The chapters cover practical solu‐ tions for reliability and safety in application to industrial coal, oil, and gas systems and transportation and cosmic systems; for systematic assessment of natural hazards; and for information security and protection against terrorist threats, including the detailed exam‐ ples. All chapters are united by the authors' efforts in finding effective system engineering solutions. This means that the book meets the main system engineering requirements of our time and the close future in the eternal conditions of uncertainty. I wish you, dear readers, the patience in understanding the ideas and their successful implementations in different

Editor - Main Researcher of the Federal Research Center "Computer Science and Control"

Senior Expert of the Main Scientific Research Test Center of the Russian Ministry of Defence;

Corresponding Member of the Russian Academy of Rockets and Artillery Sciences;

**Dr. Prof. Andrey Kostogryzov**

Moscow, Russia

Preface IX

of the Russian Academy of Sciences;

of Applied Mathematics and Certification;

Director and Scientific Leader of the Research Institute

Honored Science Worker of the Russian Federation;

Professor of the Gubkin Russian State University of Oil and Gas;

ity of systems," "safety and security," "smart systems," "system of systems," etc.

areas, not only in the example areas provided.

The second idea for reader to understand is the essence of proposed probabilistic ap‐ proaches and interpretation of the results of modeling. It may be useful for preventing a loss of benefit, wasted expenses, and unforeseen damages! Indeed, systems, production, or serv‐ ices have a quality and price on any market. They are accompanied by risks, expenses, and damages in their lifecycles. If price, expenses, and damages are understood uniformly, the terms "quality" and "risks" contain the various interests of each party. But the probabilistic predictions of "quality" and "risks" are understood at the level of possible successes or fail‐ ures during the given prognostic period. Advanced readers trace the concept of success with achieved effectiveness, with properties of reliability, safety, and other critical system attrib‐ utes. Other readers estimate success by the quantity of "like," though a degree of system purpose achievement and customer satisfaction has always been the highest level from a system engineering point of view.

Because of the inadequate prediction of quality and risks or neglect of system analysis during the early stages of the system lifecycle, wasted expenses, damages, and other serious conse‐ quences are evident often at the operation stage. Unfortunately, similar technical errors and laziness are not a rarity in real practice. The modern standards recommend the use of system analysis. And proposed models understand how to implement the required system analysis by probabilistic modeling. It can be used very opportunely to analyze predicted quality and risks for complex systems and for every element. The final step for maturity is to achieve system purpose rationally and the proposed probabilistic models can be used to solve the problems of optimization. Examples of optimization are also demonstrated in detail.

As a résumé of the basic ideas: universal probabilistic models are proposed; many of these models are supported by software tools; and the models are understandable, applicable, and they gain effects. All these ideas meet the standards requirements for solving system engi‐ neering problems in practice.

The book is intended for systems analysts, whether they be customers, designers, developers, users, or experts, and for quality, risk, safety, and security management, as well as scientists, researchers, and students. The proposed models can be used in system lifecycles to study system operation, explain and optimize system requirements, rationale technical decisions, predict and analyze quality and risks, compare different processes, adjust technological pa‐ rameters of systems, including embedded "real-time" modeling, etc. The engineering deci‐ sions, scientifically proven by the proposed models and software, supporting the models can provide purposeful essential improvement in quality and mitigation of risks and decrease expenses for created and operating systems. The models, methods, supporting software tools, and application approaches described in the book can also be used in education for system analysis and mathematical modeling on specializations, e.g., "system engineering," "opera‐ tions research," "enterprise management," "project management," "risk management," "qual‐ ity of systems," "safety and security," "smart systems," "system of systems," etc.

tion to probabilistic modeling. Many specialists refused to believe Kozma Prutkov's aphor‐ ism "One can't embrace the unembraceable, 1854," but they do already actively implement "an embrace of the unembraceable" according to international standards requirements for system engineering. The scope of these standards covers different systems (system is de‐ fined as a combination of interacting elements organized to achieve one or more stated pur‐ poses, ISO/IEC/IEEE 15288). And there are no limitations—indeed it is the age of innovations! It seems that the systems known to the reader can be covered by this definition of "system." Not simply the main "dishes," proposed by the authors of the book in the form of probabilistic models, but also many "garnishes" in the form of detailed examples of their

The second idea for reader to understand is the essence of proposed probabilistic ap‐ proaches and interpretation of the results of modeling. It may be useful for preventing a loss of benefit, wasted expenses, and unforeseen damages! Indeed, systems, production, or serv‐ ices have a quality and price on any market. They are accompanied by risks, expenses, and damages in their lifecycles. If price, expenses, and damages are understood uniformly, the terms "quality" and "risks" contain the various interests of each party. But the probabilistic predictions of "quality" and "risks" are understood at the level of possible successes or fail‐ ures during the given prognostic period. Advanced readers trace the concept of success with achieved effectiveness, with properties of reliability, safety, and other critical system attrib‐ utes. Other readers estimate success by the quantity of "like," though a degree of system purpose achievement and customer satisfaction has always been the highest level from a

Because of the inadequate prediction of quality and risks or neglect of system analysis during the early stages of the system lifecycle, wasted expenses, damages, and other serious conse‐ quences are evident often at the operation stage. Unfortunately, similar technical errors and laziness are not a rarity in real practice. The modern standards recommend the use of system analysis. And proposed models understand how to implement the required system analysis by probabilistic modeling. It can be used very opportunely to analyze predicted quality and risks for complex systems and for every element. The final step for maturity is to achieve system purpose rationally and the proposed probabilistic models can be used to solve the

problems of optimization. Examples of optimization are also demonstrated in detail.

As a résumé of the basic ideas: universal probabilistic models are proposed; many of these models are supported by software tools; and the models are understandable, applicable, and they gain effects. All these ideas meet the standards requirements for solving system engi‐

The book is intended for systems analysts, whether they be customers, designers, developers, users, or experts, and for quality, risk, safety, and security management, as well as scientists, researchers, and students. The proposed models can be used in system lifecycles to study system operation, explain and optimize system requirements, rationale technical decisions, predict and analyze quality and risks, compare different processes, adjust technological pa‐ rameters of systems, including embedded "real-time" modeling, etc. The engineering deci‐ sions, scientifically proven by the proposed models and software, supporting the models can provide purposeful essential improvement in quality and mitigation of risks and decrease expenses for created and operating systems. The models, methods, supporting software tools, and application approaches described in the book can also be used in education for system

applications can be interpreted as innovative views.

system engineering point of view.

VIII Preface

neering problems in practice.

The digital world, the Internet, and the 4th industrial revolution are changing the modern systems (planned, creating, or used) and resulting in different conditions of uncertainty in their life cycles, including operation. The changes and our growing knowledge about sys‐ tems and conditions during life cycles generate many challenges and problems concerning quantitative analysis and optimization. To meet these challenges adequately and to solve problems preventively, the probabilistic modeling in system engineering is widely used. This book demonstrates the original probabilistic ways to solve different problems by ana‐ lyzing risks for the given prognostic period in the future. The chapters cover practical solu‐ tions for reliability and safety in application to industrial coal, oil, and gas systems and transportation and cosmic systems; for systematic assessment of natural hazards; and for information security and protection against terrorist threats, including the detailed exam‐ ples. All chapters are united by the authors' efforts in finding effective system engineering solutions. This means that the book meets the main system engineering requirements of our time and the close future in the eternal conditions of uncertainty. I wish you, dear readers, the patience in understanding the ideas and their successful implementations in different areas, not only in the example areas provided.

#### **Dr. Prof. Andrey Kostogryzov**

Editor - Main Researcher of the Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences; Director and Scientific Leader of the Research Institute of Applied Mathematics and Certification; Professor of the Gubkin Russian State University of Oil and Gas; Senior Expert of the Main Scientific Research Test Center of the Russian Ministry of Defence; Corresponding Member of the Russian Academy of Rockets and Artillery Sciences; Honored Science Worker of the Russian Federation; Moscow, Russia

**Section 1**

**General Propositions for Solving Analytical**

**Problems**

**General Propositions for Solving Analytical Problems**

**Chapter 1**

Provisional chapter

**Probabilistic Modelling in Solving Analytical Problems**

DOI: 10.5772/intechopen.75686

This chapter provides some aspects to probabilistic modelling in solving analytical problems of system engineering. The historically developed system of the formation of scientific bases of engineering calculations of characteristics of strength, stability, durability, reliability, survivability and safety is considered. The features of deterministic and probabilistic problems of evaluation of the characteristics of strength, stiffness, steadiness, durability and survivability are considered. Probabilistic problems of reliability, security, safety and risk assessment of engineering systems are formulated. Theoretical bases and methods of probabilistic modelling of engineering systems are stated. The main directions of solving the problems of ensuring security and safety according to the accident risk criteria are determined. The possibilities of probabilistic modelling methods in solving the problems of strength, reliabil-

ity and safety of engineering systems are shown in practical examples.

Keywords: engineering system, multi-level concept, probability, modelling, safety,

Sustainable development of social systems and the natural environment is determined by the state and prospects of the development of engineering and technology. Modern engineering and technology are created on the basis of the achievements of fundamental scientific research. Particular importance is the development of fundamental foundations of mechanics, which is the basis for the design and produce of engineering systems. New machines and structures are creating, based on achievements of construction mechanics, theories of elasticity, plasticity and strength of materials. Multivariance of design and engineering solutions to engineering

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Probabilistic Modelling in Solving Analytical Problems

**of System Engineering**

of System Engineering

http://dx.doi.org/10.5772/intechopen.75686

survivability, security, safety, risk

Nikolay Machutov

Abstract

1. Introduction

Nikolay Machutov

Anatoly Lepikhin, Vladimir Moskvichev and

Anatoly Lepikhin, Vladimir Moskvichev and

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

#### **Probabilistic Modelling in Solving Analytical Problems of System Engineering** Probabilistic Modelling in Solving Analytical Problems of System Engineering

DOI: 10.5772/intechopen.75686

Anatoly Lepikhin, Vladimir Moskvichev and Nikolay Machutov Anatoly Lepikhin, Vladimir Moskvichev and Nikolay Machutov

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.75686

#### Abstract

This chapter provides some aspects to probabilistic modelling in solving analytical problems of system engineering. The historically developed system of the formation of scientific bases of engineering calculations of characteristics of strength, stability, durability, reliability, survivability and safety is considered. The features of deterministic and probabilistic problems of evaluation of the characteristics of strength, stiffness, steadiness, durability and survivability are considered. Probabilistic problems of reliability, security, safety and risk assessment of engineering systems are formulated. Theoretical bases and methods of probabilistic modelling of engineering systems are stated. The main directions of solving the problems of ensuring security and safety according to the accident risk criteria are determined. The possibilities of probabilistic modelling methods in solving the problems of strength, reliability and safety of engineering systems are shown in practical examples.

Keywords: engineering system, multi-level concept, probability, modelling, safety, survivability, security, safety, risk
