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Maintenance Management Informatics

Author : Dr Sead Spuzic
Senior Lecturer; Maintenance Management Programme Coordinator
CQU University

Plant Maintenance Resource Center Home Maintenance Articles

Click on this link to view the main paper on Maintenance Management Informatics by Dr Sead Spuzic.

CYBERNETICS

Cybernetics is communication, control and processing informations related to governing (regulating) a system.

From the viewpoint of Cybernetics, Informatics and System Engineering, System is a set of mutually interacting components. In other words: system is a set of mutually related relations. Actual number of components and their relations is infinite, and the complete set cannot be understood within the real timeframe. However it is possible to identify some of significant relations and to define algorithms for their control and management, by assuming that the remaining factors are insignificant or their summary effect is random (but not chaotic).

Info Science studies and collates knowledge related to cybernetics, informatics and similar constellations. System Engineering applies that knowledge in order to organise, improve, control and govern actual systems.

System Science is applied to the analysis of all aspects of systems, both manmade and non-manmade. One example is a lake with incoming and out coming flow including its flora an fauna. Another example would be a complex electrical network with feedback loops, in which the effects of a process cause changes in the source of the process

Cybernetics includes registering, collating, recording, processing and distributing information related to system (in our case - the maintenance system) in order to document, control and improve it. This includes planning, evidence, measuring, analyzing, observing, deciding and publishing. Difference between the cybernetics and informatics is in that, while informatics comprises all aspects of informations, cybernetics comprises informatics plus other relations affecting the significant (predefined) processes within a system.

Cybernetic media are means that enable managing, complying and acting in accordance to orders, communication, evidence and other function of the whole system.

The term cybernetics comes from the ancient Greek word kybernetikos -"good at steering". In the 19th-century, the physicist A-M Ampère, in his classification of sciences, suggested that the science of the control of governments be called cybernetics while another physicist J C Maxwell pointed out that "governor" is derived, via Latin, from the same Greek word that gives rise to the term cybernetics.. This term was used by the mathematician N Wiener and as the title for his book in 1948: cybernetics was defined as "the science of control and communications in the .. machine". This definition relates cybernetics with the theory of automatic control and with the physiology of the nervous system. Subsequently, the computer and the areas of mathematics related to it (e.g., mathematical logic) had a great influence on the development of cybernetics. The reason is that the computer can be used not only for automatic calculation but also for conversions of information, including info processing used in system control. The narrower view, common in the West, defines cybernetics as the science of control of complex systems (technical, biological, and social). In Western countries, particular emphasis is given to those aspects of cybernetics that are used in the generation of control system in technology and in living organisms. In addition to cybernetics, the science of computers and the general rules of info processing have been developed in the West (English-computer science; French-informatique). The broader interpretation of the subject of cybernetics that prevailed in Russia and former "socialistic countries" includes not only control but all forms of info processing. This definition includes Western computer science as one of the components of cybernetics.27

The Science of cybernetics came into being when concepts of information, feedback, and control were generalized from specific applications (e.g. in engineering) to system in general, including . abstract intelligent processes and language. Theory of Artificial Intelligence came into being when the concept of universal computation and the availability of digital computing machines were combined with the the cultural view of the brain as a computer. Artificial Intelligence (AI) uses computer technology to strive toward the goal of machine intelligence and considers implementation as the most important result. Cybernetics uses epistemology (the limits to how we know what we know) to understand the constraints of any medium (e.g. technological) and considers powerful descriptions as the most important result.28

SYSTEM ENGINEERING

General notions

System Engineering (also Systems Engineering) involves design, management, control and optimisation of a total system. The system engineering process is a structured, disciplined, and documented technical effort through which system products and processes are simultaneously defined and developed. Most effective system engineering utilises multidisciplinary teamwork to implement holistic strategy to integrated product and process development. Examples of engaged disciplines include electr(onic)ical engineering, communications theory, cybernetics, and computer theory.

System engineering applies knowledge from other branches and disciplines in effective combination to solve multifaceted engineering problems. It is related to operations research but differs from it in that it is more a planning and design function, frequently involving innovative solutions. Probably the most important goal of system engineering is its application and development of new technological possibilities with the specific objective of putting them to use as rapidly as economic, technical and other social considerations permit. Nowadays system engineering is an accepted academic discipline taught in many universities throughout the world and supported by worldwide professional societies.4

System engineers frequently have an electronics or communications background and make extensive use of computers and communications technology. Fundamentally, system engineering is an interdisciplinary procedure for putting separate techniques and bodies of knowledge together to efficiently achieve a predefined goal. In general, a system engineering approach is likely to differ from a conventional design approach by exhibiting increased generality in its logical framework and increased concern with the fundamental objectives to be achieved (system engineer is likely to ask both "why" and "how", rather than merely "how").4

Typical questions posed by system engineer include the following points:

  • What is the general problem which need to be solved?
  • What are the influencing factors and limitations, including their hierarchy?
  • How will we know when we have adequately defined the problem?
  • How will we know when the problem is solved?

The systems with which a system engineer is concerned are first of all man-made. Second, their components interact so extensively that a change in one part is likely to affect many others. In addition, their inputs are normally stochastic. Systems may also vary depending on the amount of human judgment that enters into their operation. Thus, system performance must be evaluated as a statistical average of the responses to a range of possible inputs.4

Deterministic systems such as electrical circuits, automated production equipment, or robots that may operate in a completely determinate fashion. At the other extreme, there are management and control systems, e.g. for business purposes, in which machines do most of the work but with human supervision and decision making present at critical points.4

The development of system engineering

Mathematical modeling

It is a common procedure in science (and elsewhere) to list all the factors that might affect a given situation and select from the complete list those that appear critical. Mathematical modeling, perhaps the most basic tool in system engineering, is a technique encountered in any branch of science that has become sufficiently quantitative.4

Operations research has particularly influenced system engineering. Because operations research is based on applications of deterministic models to optimise the use of (traditionally, the existing) equipment, technological uncertainties are not taken in account. System engineering, on the other hand, takes in account that such uncertainties may be important (especially when planning and commencing new equipment).4

Communications and electronics

The general field of communications, particularly commercial telephony, is traditional environment where system engineering first appeared as an explicit discipline. Bell Telephone Laboratories incorporated in 1925 two principal engineering divisions: Apparatus Development and System Development. In 1950s system engineering emerged as well established strategy used to redefine the policy and structure of the research and development. The system engineer had a multitude of functions, with special emphasis on effective utilisation of scientific and technical advances in planning new communication systems. This particular set of ideas, while reflecting the special needs of telephony, had advanced rapidly to virtually all aspects of society.4

The recent development of system engineering stemmed, in large part, from the impact of great advances in communications and electronics. An automatic control system is a good example. A control system has the prime characteristic that the components interact extensively and that the system as a whole has certain properties-e.g., stability-that cannot be said to adhere to any individual component.4

The development of info theory as a fundament for communications engineering, was also influential in shaping the evolution of system engineering. The individual subsystems were found to be held together within the complex system by communication channels. Concepts of info transfer from one part of the system to another proved useful in holistic understanding the operation of the whole structure.4

Computers and system engineering

System engineering profited strongly from the advent of computers and the development of programming languages. They provided new tools for analysing complex systems by means of extensive calculations or direct simulation. Computers have enabled storing, processing and transmitting large amounts of data within the real timeframe.4

Control and feedback questions were also important aspects of the overall system. The whole system is in fact a gigantic feedback loop. There is a closed feedback loop from operation to computer and back. There are also peripheral feedback loops and the dynamic response of the system. The analysis of such elaborate dynamical system involving interlaced feedback paths has become an important special part of the general system area.4

The system approach also became increasingly identified with management functions. System engineers have become involved both the initial planning of a project and its subsequent management. Planning, programming and budgeting techniques were developed to provide vital combinations of system engineering and financial management.4

System engineering has developed to explore feedback control system in large-scale automated production facilities, such as steel-rolling mills and petroleum refineries. These applications stressed computer-based management information and control system. In more recent years the system approach has occasionally been applied to much larger civilian enterprises, such as the planning of new cities.4

System engineering techniques, tools, and procedures

Algorithms

Algorithm is a step-by-step procedure or a sequence of formulas for solving a problem. In other words, algorithm is logically specified mapping defining achievable steps between objects belonging to a finite set in order to achieve a predefined goal. Algorithms can be probabilistic, deterministic, heuristic, on-line algorithm, off-line algorithm.

For example, to calculate sum of a set of natural numbers up to a number N, one may use the following algorithm:

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However simple, the above operation includes a sequence of steps that are yet to be specified. Namely the following steps should be defined:

  1. Denote value "1" to a variable x1.
  2. Store value of x1 in suitable storage file
  3. Denote value "x1+1" to a variable x2.
  4. Store value of x2 in the above storage file
  5. Repeat the above steps 2) and 3) until the index k = N
  6. Perform addition of all stored values xk
  7. Deliver the result (the required info) to the requestor (e.g display the result on a monitor or print it on paper).

It may appear that the above description (algorithm) is somewhat too extensive and that comprises some unnecessary explanations. In fact, the above instructions are still incomplete, and this becomes obvious when the above task is given to a machine. Certain general conventions will always be assumed as known and agreed prior to specifying any algorithm. However the whole procedure should be free from any ambiguities or possibilities to interpret some command in several manners, without a clear criterion is given to specify which option is due.

The above aspects have become very obvious when computers and computing programs have become used for automatically performing various increasingly complex tasks.

Modeling and optimization

Perhaps the most fundamental technique is the flow diagram, or flowchart, a graphical display composed of boxes representing individual components or subsystem of the complete system, plus arrows from box to box to show how the subsystem interact. Graphical representation of any process is always better and more meaningful than its representation in words.

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Fig 4-1: A flow-chart

Though such a representation is very useful in an initial study, it is essentially qualitative. A more effective approach in the long run is construction of a so-called mathematical model, which consists of a set of equations, or other instructions, describing the interactions within the system in quantitative terms. It is not necessary for the mathematical model to be exact, as long as it indicates trends (effects of significant factors). It frequently consists of piecewise linear approximations to basically nonlinear situations (i.e., a series of short straight lines that roughly approximate a curve). After the model has been constructed and checked, a number of mathematical techniques can be employed (including straightforward enumeration and computing) to find out what it says about the actual operation of the system. Often these calculations will lead to developing a stochastic model.4

When the components or subsystem interact significantly, it may be possible to achieve essentially the same final performance in many different ways. Limited performance by one subsystem may be compensated by superior performance somewhere else. These optimization studies, called trade-offs, are important in suggesting how to achieve a given result in the most economical manner under the dynamically changing limitations. They are equally valuable in suggesting whether or not the proposed result is in fact a reasonable goal to aim for. It may be found, for example, that a modest reduction in performance will permit radical savings in overall cost or, conversely, that the postulated equipment is capable of much better performance than is asked of it, at only nominally greater expense. It may also turn out that the equipment can supply useful functions not originally contemplated. Computing system, for example, can usually perform extra chores of record keeping at little increased cost. For all of these reasons, studies of such variables are an important part of system engineering, both in the early exploratory phases of a project and in the final design.4

Identifying objectives

The formulation of suitable objectives for the final system is important part of the system engineering process. A sufficiently clear, precise, and comprehensive statement of objectives is a prerequisite for successful engineering studies. Unless the situation has been well explored in the past, the real choices are not likely to be obvious when the work begins. This usually involves computations and consultation with others. Because the final statement must reflect value judgments as well as purely technical considerations, the system engineer acts as a working focus and catalyst in a multidisciplinary team.4

Most system have multiple objectives, often in conflict with one another. In the design of transport aircraft, for example, there are a multitude of desirable characteristics, such as range, speed, payload, and safety, to be maximized, as well as undesirable characteristics, such as cost, total weight, noise generation and air pollution, to be minimized. Because the same design cannot do the best job in all of these directions, a compromise achieving the most desirable overall performance is required. The most attractive compromise can be reached only by cross-disciplinary analysis of multiple factors and their interactions.4

Special problems in defining objectives may arise when an existing methods and techniques are transplanted to some new disciplinary area. An example is the application of electronics such as computer techniques to medicine and education. It seldom happens in such cases that the best system is based on a simple one-for-one substitution, such as direct replacement of a classroom teacher by electronic hardware and computer-assisted instruction materials. It is much more likely that the most effective plan will turn out to be a rather innovative mixture of the old and the new.4

A design example

The design of the commercial transport plane is an example of a system engineering problem. In such a design the aerodynamic lift, the drag of fuselage and wings, the control apparatus, the propulsion system, and such auxiliary hardware as the design of pilots chair and configuration of switches and monitors all interact substantially.

If a possible advanced solutions are considered, such as an improvement in propulsion or aerodynamics, all elements and aspects of the total system, and the interactions among them, must be considered to determine how the novel solutions might best be applied in a new airplane design. The central system engineering question then would probably encompass the relation between the available new plane characteristics and the needs of the existing air transportation system. Clearly, such an investigation can be made only by going to one of the upper levels in the system hierarchy.4

Finally, to operate the new airplane successfully, a whole series of supporting functions may be required, including routine checkout, maintenance, and spare parts supply, in addition to functions directly involved in the plane's flight.4

To make adequate comparisons between competing objectives, a logical frame of reference is needed. Thus, the system engineer may study many situations in the framework of more than one system or a whole hierarchy of system of steadily increasing generality. In the example of an airplane, the airplane itself is a possible system, as are the group of planes owned by one airline, the total number of airplanes in a particular country, and that nation's transportation facilities. Though the simplest system-the airplane itself-is a satisfactory reference for specific design problems, a more general framework may be needed to approach broader problems. Thus the individual airplane designer may seek to ameliorate air-traffic congestion by improving airplane takeoff and landing characteristics, permitting better utilization of existing airports. The airlines in turn may suggest construction of more and better airports. From the holistic viewpoint of the transportation system, the best approach might be to invest more resources in high-speed rail facilities to substitute part of the air-traffic.4

User orientation

The stress on system objectives is a consequence of strong focus on the user. System objectives naturally relate to overall performance, which is what the final user is interested in. In line with this approach, system engineering is likely to give special consideration to such qualities as reliability, ease of maintenance, and convenience in operation. Moreover, the final step of a system engineering project is typically an evaluation that aims to find out how well the system works in the hands of the user.4

Tools

Design of a complex system includes applications of probability, computing, system logic, queuing theory, game theory, linear programming, cybernetics, group dynamics, simulation, info theory, servomechanism theory, human engineering, decision theory, computer science, nonlinear programming, econometrics, and communications theory, along with any other scientific discipline suitable for addressing relevant aspects.

These tools are all essentially mathematical in nature. The system engineer utilises knowledge and skills of other sorts as well. The mathematical modelling and defining objective functions, which are conducted at the onset of system analysis, are only satisfactory to the extent that they meaningfully represent the real physical situation. The adequacy of a mathematical model in this sense, however, is a matter of physical or engineering rather than mathematical judgment. System engineer may also need to know something about experimental procedures in general and, in particular, about ways of maximizing the amount of info from a given testing program. This is particularly likely to happen in urgent high-risk projects, like nuclear power generation, in which intermediate testing failures are bound to occur, and the system engineer then must decide what to do next. A closely related question is the monitoring of testing procedures for routine quality control purposes. This also reflect a basic user orientation.4

When the system engineer's job is defined as including significant management responsibility, some acquaintance with modern management techniques is an obvious requirement. The techniques of particular importance are those that bear most directly on cost figuring and scheduling technological developments.4

Finally, system engineering is used in new situations that may involve the application of new discoveries in science or technology to existing technical areas or the application of known science or technology in new contexts. In either case the system engineer obviously needs considerable knowledge of the fields involved in order to make reliable plans. It is apparent that no single person is likely to meet all of these specialized qualifications. Thus, system engineering on any significant scale almost invariably involves a team approach.4

Applications of system engineering

Many useful systems are, in effect, modifications of previous designs. The proportions of the subsystem may be changed, but no substantial function has been added or left out. The basic task of the system engineer in such a situation is relatively straightforward; it is essentially a matter of reoptimizing the existing design to meet the new conditions.4

In other circumstances, however, the basic system concept represents a more radical break with the past. The new concept may involve the introduction of new functions or the realization of old functions in new ways.4

Radically new system concepts are like inventions in other engineering fields. Usually this means a substantial advance in overall performance, more than would be expected from a modest reproportioning of a known system. On the other hand, in many cases it is impossible to predict accurately in advance of the development just what performance may be achievable in one or more of the critical elements of the new system. This leaves the system engineer with a special problem in planning, which is usually addressed by establishing a minimum acceptable ("pessimistic") level of performance for the critical elements. The rest of the system is considered so that whatever is eventually achieved beyond this pessimistic level will be treated as growth potential in the overall capabilities of the system. Thus definitive optimization studies may be postponed until the system is better understood.4

Long-term system development

In many situations the elements of continuity (longevity, maintainability and sustainability) are matters of special importance. System analysis may require to work on a series of similar problems as part of a long-term effort. In the case of telephone networks, system engineering groups have been set up permenently over many years as permanent parts of the overall organizational structure, each group having cognizance over some wide area of telephone technology.4

Major technical advances may require many years between the original discovery or conception and the time when a practical design becomes feasible. The system engineers keep in touch with the design and research work as it progresses throughout this tedious period. They can influence the investigation simply by noticing uncertainties and errors that need checking and correction for a project to succeed. It often happens that an extensive program of systematic measurements is launched before a new system conception can be implemented, even when the basic conception is well established.4

Evolutionary cybernetics studies the origin and development of purposeful structures in Society and Technology. Evolutionary cybernetics is the study of how the processes of variation and selection give rise to organisation. Aspects such as the dynamics of distinctions, connections, variety, selection and limitations affect the systems that emerge out of unstructured aggregates.

In many cases, technology suggests several competitive approaches to the same problem. There is generally no need to make a premature choice between them. Rather, several options can be followed until it is clear that one is superior or, perhaps, that each has its particular niche in the marketplace.4

Top-down and Bottom-up strategies in system analysis
The top-down method generates core processes from the strategic business fields. These processes, on the highest level of abstraction, are successively refined in the course of modelling. This is substantially the process of deduction (reasoning and drawing notions starting from general level towards the 'lower' particular levels). The advantage of this method is that the developed business processes are well aligned with strategic viewpoints.

The traditional view is that deduction proceeds "from the general to the specific" or "from the universal to the particular" (this definition has been abandoned as incorrect by some logicians7).

The disadvantage is that the hierarchical refinement will actually deduce process structures that do not perform optimally at the reached 'lower' level. This is particularly the case when the resulting interdependences between the partial processes are not recognized and the conflicting requirements are not identified.

The bottom-up method is based on the entirety of all planned activities. For every identified activity, process models are generated, from which the process structures on 'higher' levels are derived through grouping. The business processes are later divided into core processes, and support processes. This is substantially a process of traditional induction (reasoning from detailed facts to general principles; from a part to a whole). Some sources7 declare both above definitions of 'deduction' and 'induction' as obsolete; in this treatise the traditional terms will be used until consensus is reached, rather than exclusively using terms 'bottom-up' and 'top-down'.6

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