20 1 Design Integrity Methodology
Designing for maintainability, as it is applied to an item of equipment, includes the
aspects of testability, repairability and inter-changeabilityof an assembly’s inherent
components. In general, the concept of designing for maintainability is concerned
with the restoration of equipment that has failed to perform over a period of time.
The performance variable used in the determination of maintainability that is con-
cerned with the measure of time subject to equipment failure is the mean time to
repair (MTTR).
Thus, besides providing for visibility, accessibility, testability, repairability and
inter-changeability, designing for maintainability also incorporates an assessment
of expected performance in terms of the measure of MTTR in relation to the per-
formance capabilities of the equipment. Designing for maintain a bility during the
preliminary design phase would be to minimise the MTTR of a system by ensuring
that failure of an inherent assembly to perform a specific duty can be restored to its
expected performance over a period oftime. Similarly, designing for maintainability
during the detail design phase would be to minimise the MTTR of an assembly by
ensuring that failure of an inherent component to perform a specific function can be
restored to its expected initial state over a period of time.
d) Designing for Safety
Traditionally, assessments of the risk of failure are made on the basis of allow-
able factors of safety obtained from previous failure experiences, or from empirical
knowledge of similar systems operating in similar anticipated environments. Con-
ventionally, the factor of safety has been calculated as the ratio of what are assumed
to be nominal values of demand and capacity. In this context, demand is the resul-
tant of many uncertain variables of the system under consideration, such as loading
stress, pressures and temperatures. Similarly, capacity depends on the properties of
materials strength, physical dimensions, constructability,etc. The nominal values of
both demand and capacity cannot be determined with certainty and, hence, their ra-
tio, giving the conventional factor of safety, is a random variable. Representation of
the values of demand and capacity would thus be in the form of probability distribu-
tions whereby, if maximum demand exceeded minimum capacity, the distributions

levels of modelling sophistication in the practical application of the theory:
• RAMS analysis model (product assurance) for an engineering design project
of an environmental plant for the recovery of sulphur dioxide emissions from
a metal smelter to produce sulphuric acid as a by-product. The purpose of im-
plementing the RAMS analysis model in this target engineering design project
is to validate the developed theory of design integrity in designing for reliabil-
ity, availability, maintainability and safety, for eventual inclusion in intelligent
computer automated methodology using artificial intelligence-based (AIB) mod-
elling.
• OOP simulation model (process analysis)for an engineeringdesign super-project
of an alumina plant with establishment costs in excess of a billion dollars. The
purpose of implementing the object oriented programming (OOP) simulation
model in this target engineering design p roject was to evaluate the mathemati-
cal algorithms developed for assessing the reliability,availability, maintainability
and safety requirements of complex process systems, as well as for the complex
integration of process systems, for eventual inclusion in intelligent computer au-
tomated methodology using AIB modelling.
• AIB blackboard model (design review) for an engineeringdesign super-project of
a nickel-from-laterite processing plant with establishment costs in excess of two
billion dollars. The AIB blackboard model includes intelligent computer auto-
mated methodologyfor application of the developed theory and the mathematical
algorithms.
22 1 Design Integrity Methodology
1.2.1 Development of Models and AIB Methodology
Applied co mputer modelling inclu des up-to-date object oriented software program-
ming applications inco rporatin g integrated systems simulation modelling, and AIB
modelling including knowledge-based expert systems as well as blackboard mod-
elling. TheAIB modelling provides for automated continualdesign reviews through-
out the engineeringdesign p rocess on the basis of concurrentdesign in an integrated
collaborative engineering design environment. Engineering designs are composed

how the design process should proceed. This process ensures an optimal solution
and is usually the construct of the initial design specification. It therefore involves
maintaining numerous candidate solutions to specific design problems in parallel,
whereby designers need to be adept at gen erating and evaluating a range of candi-
date solutions.
1.2 Artificial Intelligence in Design 23
The term satisficing is used to describe how designers sometimes limit their
search of the design solution space, possibly in response to technology limitations,
or to reduce the time taken to reach a solution because of schedule or cost con-
straints. Designers may opportunistically deviate from an optimal strategy, espe-
cially in engineering d esign where, in many cases, the design may involve early
commitment to and refining of a sub-optimal solution. In such cases, it is clear that
satisficing is often advantageous due to potentially r educed costs or where a satis-
factory, rather than an optimal design is required. However, solving complex design
problems relies heavily on the designer’s knowledge, gained through experience, or
making use of previous design solutions.
The concept of reuse in design was traditionally limited to utilising personal ex-
perience, with reluctance to copy solutions of other designers. The modern trend in
engineering design is, however, towards more extensive design reuse in a collabo-
rative environment. New computing technology provides greater opportunities for
design reuse and satisficing to be applied, at least in part, as a collaborative, dis-
tributed activity. A large amount of current research is concerned with developing
tools and methodologies to support design teams separated by space and time to
work effectively in a collaborative design environment.
a) The RAMS Analysis Model
The RAMS analysis model incorporates all the essential preliminaries of systems
analysis to validate the developed theory for the determination o f the integrity of
engineering design. A layout of part of the RAMS analysis model of an environ-
mental plant is given in Fig. 1.1.
The RAMS analysis model includes systems breakdown structures, process func-

Implementation o f the various models covered in this handbook predominantly fo-
cuses on determining the applicability and benefit of automated continual design
reviews throughout the engineering design p rocess. This hinges, however, upon
a broader understanding of the principles and philosophy of the use of artificial
intelligence (A I) in engineering design, pa rticularly in which new AI modelling
techniques are applied, such as the inclusion of knowledge-based expert systems
in blackboard models. Although these modelling techniques are described in d etail
later in the handbook, it is essential at this stage to give a brief account of artificial
intelligence in engineering design.
The application of artificial intelligence (AI) in engineering design, through ar-
tificial intelligence-based ( AIB) computer modelling, enables decisions to be made
about acceptable d esign performance by considering the essential systems design
criteria, the functionality of each particular system, the effects and consequences of
potential and functional failure, as well as the complex integration of the systems as
a whole. It is unfortunate that the growing number of unfulfilled promises and ex-
pectations about the capabilities o f artificial intelligence seems to have damaged the
credibility of AI and eroded its true contributions and benefits. The early advances
26 1 Design Integrity Methodology
Fig. 1.3 Layout of the AIB blackboard model
of expert systems, which were based on more than 20 years of research, were over-
extrapolated by many researchers looking for a feasible solution to the complexity
of integrated systems design. Notwithstanding the problems of AI, recent artificial
intelligence research has produced a set of new techniques that can usefully be em-
ployed in determining the integrity of engineering design. This does not mean that
AI in itself is sufficient, or that AI is mutually exclusive of traditional engineering
design. In order to developa proper perspective on the relationship between AI tech-
nology an d engineering design , it is necessary to establish a framework that provides
the means by which AI techniques can be applied with conventional engineering de-
sign. Knowledge-based systems provide such a framework.
a) Knowledge-Based Systems

and the best expert system is one that hasbeen created through a close scrutiny of the
expert’s domain by a ‘knowledgeable’ knowledge engineer. However, the question
often asked is which kinds of problems are most amenable to this type of approach?
Inevitably,problemsrequiringknowledge-intensiveproblemsolving,where years
of accumulated experience produce good performance results, must be the most
suited to such an approach. Such domains have complex fact structures, with large
volumes of specific items of information, organised in particular ways. The domain
of engineering design is an excellent example of knowledge-intensiveproblem solv-
ing for which the application of expert systems in the design process is ideally
suited, even more so for determining the integrity of engineering design. Often,
though, there are no known algorithms for approaching these problems, and the do-
main may be poorly formalised. Strategies for approaching design problems may
be diverse and depend on particular details of a problem situation. Many aspects of
the situation need to be determined during problem so lving, usually selected from
a much larger set of possible needs of which some may be expensive to determine—
thus, the significance of a particular need must also be considered.
28 1 Design Integrity Methodology
c) Expert Systems in Engineering Design Project Management
The advantages of an expert system are significant enough to justify a major effort
to develop these. Decisions can be obtained more reliably and consistently, where an
explanation of the final answers becomes an important benefit. An expert system is
thus especially useful in a consultation mode of complex engineering designs where
obscure factors may be overlooked, and is therefore an ideal tool in engineering
design project management in which the following important areas of engineering
design may be impacted:
• Rapid checking of preliminary design concepts, allowing more alternatives to be
considered;
• Iteration over the design process to improve on previous attempts;
• Assistance with and automation of complex tasks and activities of the design
process where expertise is specialised and technical;

the basic ingredients for assisting or actually doing design is still an open research
topic.
e) Blackboard Models
Early expert systems used rules as the basic data structure to address h euristic
knowledge. From the rule-based expert system, there has been a shift to a more
powerful architecture based on the notion of cooperating experts (termed black-
board models) that allows for the integration of algorithmic design approaches with
AI techniques. Blackboard models provide the means by which AI techniques can
be applied in determining the integrity of engineering designs.
Currently,oneof the main areas of developmentis to p rovide integrativemeans to
allow various design systems to communicate with each other both dynamically and
cooperatively while working on the same design problem from different viewpoints
(i.e. concurrent design). What this amounts to is having a diverse team of experts or
multidisciplinary groups of design engineers, available at all stages of a design, rep-
resented by their expert systems. This leads to a design process in which technical
expertise can be shared freely in the form of each group’s expert system (i.e. col-
laborative design). Such a design process allows various groups of design engineers
to work on parts of a design problem independently, using their own expert sys-
tems, and accessing the expert systems of other disciplinary groups at those stages
when group cooperation is required. This would allow one disciplinary group (i.e.
process/chemical engineering) to produce a design and obtain an evaluation of the
design from other disciplinary groups (i.e. mechanical/electrical engineering), with-
out involving the people concerned. Such a design process results in a much more
rapid consideration of major design alternatives, and thus improves the qu ality of
the result, the effectiveness of the design review process, and the integrity of the
final design.
AclassofAI tools constructed along these lines is the blackboard model,which
provides for integrated design data management, and for allowing various knowl-
edge sources to cooperate in data development, verification and validation, as well
as in information sharing (i.e. concurrent and collaborative design). The blackboard