Systems Engineering(redirected from Automation engineer)
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systems engineering[′sis·təmz ‚en·jə‚nir·iŋ]
the scientific technical discipline encompassing various aspects of designing, building, testing, and operating complex, or large-scale, systems. In the development of large-scale systems, problems arise that are related not only to the characteristics of the component parts but also to the regularities that govern the functioning of the object as a whole. This engenders a broad range of specific tasks, including determination of the overall structure of the system, organization of interaction between subsystems and elements, evaluation of the influence of the environment, and selection of optimum functioning regimes and optimum control of the system. As systems become more complex, greater significance is attached to system-wide questions, which represent the primary content of systems engineering. The theory of large-scale systems—a comparatively new scientific discipline—is the scientific, primarily mathematical, basis of systems engineering.
The designing of large-scale systems is usually organized in a specific, two-stage fashion: macrodesigning, during which the functional-structural problems of the system as a whole are resolved, and microdesigning, involving development of the system’s elements as physical units of equipment. Systems engineering combines points of view, approaches, and techniques related to problems in the macrodesigning of large-scale systems.
Macrodesigning begins with the formulation of the problem. The formulation includes at least three primary divisions: determination of the goals in building the system and of the range of problems the system will solve; assessment of factors acting on the system and determination of their characteristics; and selection of characteristics of system efficiency. The goals and tasks of the system are determined on the basis of practical needs with due regard for the special features and trends of technological progress and national economic expediency. Experience with the use of similar existing systems and a clear understanding of the role in the national economy of the system being designed are very important. In addition to experience in operating analogous systems, statistical data obtained by special experimental research are used to assess the external and internal factors acting on the system. Quantitative characteristics that define the degree of the system’s suitability for the problems assigned are chosen as the indexes of efficiency. For example, in an instrument landing system for aircraft, the efficiency index may be the probability of a successful landing. The efficiency index for a long-distance telephone system may be the average waiting time for connection, and for a production process, the average number of articles produced per shift. Date gained from studying the goals, tasks, and results of experiments are used to substantiate the design assignment for the development of a system.
In accordance with the design assignments, designers outline one or several system variations meriting further consideration and detailed investigation. Analysis of the variations (systems analysis) is conducted from the results of mathematical modeling. In practice, simulation modeling of the system by digital computers is usually preferred. The simulation model is an algorithm, which the digital computer uses to produce data that characterize the behavior of elements of the system and the interaction of the elements in the functioning of the system. The information obtained makes it possible to determine the system’s efficiency, substantiate the system’s optimum structure, and prepare recommendations for optimization of the variations studied. There are also analytic methods of evaluating the properties of large-scale systems based on the results of using the theory of stochastic processes.
The designers of large-scale systems are broadly trained specialists called systems engineers. They have a sufficient grounding in some concrete technical field, such as machine building, electronics, the food industry, or aviation, advanced mathematical training, and a knowledge of the fundamentals and applications of computer technology, control automation, and operations research. In addition to systems engineers, a macrodesign group for large-scale systems usually includes specialists in systems analysis and mathematical modeling as well as engineers capable of organizing interaction between elements of the system.
There are important special features in testing complex systems. Full-scale tests are only used to evaluate the parameters of the key elements of the system. In integrated tests of the system, however, simulation models play an important role. Specifically, such models are the basis for constructing simulators of environmental effects and generators of dummy signals and messages; they simulate those processes of the functioning of the elements that would not be feasible in a full-scale test.
REFERENCESGoode, H. H., and R. E. Machol. Sistemotekhnika: Vvedenie v proektirovanie bol’shikh sistem. Moscow, 1962. (Translated from English.)
Spravochnik po sistemotekhnike. Moscow, 1970. (Translated from English.)
Buslenko, N. P., V. V. Kalashnikov, and I. N. Kovalenko. Lektsii po teorii slozhnykh sistem. Moscow, 1973.
N. P. BUSLENKO
A management technology involving the interactions of science, an organization, and its environment as well as the information and knowledge bases that support each. The purpose of systems engineering is to support organizations that desire improved performance. This improvement is generally obtained through the definition, development, and deployment of technological products, services, or processes that support functional objectives and fulfill needs.
Systems engineering has triple bases: a physical (natural) science basis, an organizational and social science basis, and an information science and knowledge basis. The natural science basis involves primarily matter and energy processing. The organizational and social science basis involves human, behavioral, economic, and enterprise concerns. The information science and knowledge basis is derived from the structure and organization inherent in the natural sciences and in the organizational and social sciences.
Systems engineering may also be defined as management technology to assist and support policy making, planning, decision making, and associated resource allocation or action deployment. It accomplishes this by quantitative and qualitative formulation, analysis, and interpretation of the impacts of action alternatives upon the needs perspectives, the institutional perspectives, and the value perspectives of clients to a systems engineering study. Each essential phase of a systems engineering effort—definition, development, and deployment—is associated with formulation, analysis, and interpretation. These enable systems engineers to define the needs for a system, develop the system, and deploy it in an operational setting and provide for maintenance over time, all within time and cost constraints.
Contemporary systems engineering focuses on tools, methods, and metrics, as well as on the engineering of life-cycle processes that enable appropriate use of these tools to produce trustworthy systems. There is also a focus on systems management to enable the wise determination of appropriate processes. See Systems integration
Much contemporary thought concerning innovation, productivity, and quality can be cast into a systems engineering framework. This framework can be valuably applied to systems engineering in general and information technology and software engineering in particular. The information technology revolution provides the necessary tool base that, together with knowledge management–enabled systems engineering and systems management, allows the needed process-level improvements for the development of systems of all types. The large number of ingredients necessary to accomplish needed change fit well within a systems engineering framework. Systems engineering constructs are useful not just for managing big systems engineering projects according to requirements, but for creative management of the organization itself. See Information systems engineering, Large systems control theory, Quality control, Systems analysis