1.2 Artificial Intelligence in Design Analysis of Target Engineering Design Projects A stringent approach of objectivity is essential in implementing the theory of design integrity in an
Trang 1Designing for maintainability, as it is applied to an item of equipment, includes the aspects of testability, repairability and inter-changeability of 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 maintainability 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 of time 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 distribudistribu-tions would overlap with a non-zero probability of failure.
A convenient way of assessing this probability of failure is to consider the
differ-ence between the demand and capacity functions, termed the safety margin, a ran-dom variable with its own probability distribution Designing for safety, or the mea-sure of adequacy of a design, where inadequacy is indicated by the meamea-sure of the probability of failure, is associated with the determination of a reliability index for
items at the equipment and component levels The reliability index is defined as the number of standard deviations between the mean value of the probability distribu-tion of the safety margin, where the safety margin is zero It is the reciprocal of the coefficient of variation of the safety margin
Designing for safety furthermore includes analytic techniques such as genetic al-gorithms and/or artificial neural networks (ANN) to perform multi-objective
Trang 2optimi-sations of engineering design problems The use of genetic algorithms in designing for safety is a new approach in determining solutions to the redundancy allocation problem for series-parallel systems design comprising multiple components Artifi-cial neural networks in designing for safety offer feasible solutions to many design problems because of their capability to simultaneously relate multiple quantitative
and qualitative variables, as well as to form models based solely on minimal data.
1.2 Artificial Intelligence in Design
Analysis of Target Engineering Design Projects
A stringent approach of objectivity is essential in implementing the theory of design integrity in any target engineering design project, particularly with regard to the numerous applications of mathematical models in intelligent computer automated methodology Selection of target engineering projects was therefore based upon il-lustrating the development of mathematical and simulation models of process and equipment functionality, and development of an artificial intelligence-based (AIB) blackboard model to determine the integrity of process engineering design
As a result, three different target engineering design projects are selected that relate directly to the progressive stages in the development of the theory, and to the 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, availabilreliabil-ity, 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 engineering design 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 project 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 engineering design 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 methodology for application of the developed theory and the mathematical algorithms
Trang 31.2.1 Development of Models and AIB Methodology
Applied computer modelling includes up-to-date object oriented software
program-ming applications incorporating integrated systems simulation modelling, and AIB
modelling including knowledge-based expert systems as well as blackboard
mod-elling The AIB modelling provides for automated continual design reviews through-out the engineering design process on the basis of concurrent design in an integrated collaborative engineering design environment Engineering designs are composed
of highly integrated, tightly coupled components where interactions are essential to the economic execution of the design
Thus, concurrent, rather than sequential consideration of requirements such as
structural, thermal, hydraulic, manufacture, construction, operational and
mainte-nance constraints will inevitably result in superior designs Creating concurrent de-sign systems for engineering dede-signers requires knowledge of downstream
activi-ties to be infused into the design process so that designs can be generated rapidly
and correctly The design space can be viewed as a multi-dimensional space, in
which each dimension has a different life-cycle objective such as serviceability or integrity
An intelligent design system should aid the designer in understanding the in-teractions and trade-offs among different and even conflicting requirements The intention of the AIB blackboard is to surround the designer with expert systems that provide feedback on continual design reviews of the design as it evolves throughout
the engineering design process These experts systems, termed perspectives, must
be able to generate information that becomes part of the design (e.g mass-flow bal-ances and flow stresses), and portions of the geometry (e.g the shapes and dimen-sions) The perspectives are not just a sophisticated toolbox for the designer; rather,
they are a group of advisors that interact with one another and with the designer, as well as identify conflicting inputs in a collaborative design environment
Implemen-tation by multidisciplinary remotely located groups of designers inputs design data and schematics into the relevant perspectives or knowledge-based expert systems, whereby each design solution is collaboratively evaluated for integrity Engineering design includes important characteristics that have to be considered when develop-ing design models, such as:
• Design is an optimised search of a number of design alternatives.
• Previous designs are frequently used during the design process.
• Design is an increasingly distributed and collaborative activity.
Engineering design is a complex process that is often characterised as a top-down search of the space of possible solutions, considered to be the general norm of 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 generating and evaluating a range of candi-date solutions
Trang 4The 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 design 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 reduced 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 of 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-tion definifunc-tion, determinafunc-tion of failure consequences on system performance, de-termination of process criticality, equipment functions definition, dede-termination of failure effects on equipment functionality, failure modes effects and criticality anal-ysis (FMECA), and determination of equipment criticality
b) The OOP Simulation Model
The OOP simulation model incorporates all the essential preliminaries of process analysis to initially determine process characteristics such as process throughput,
output, input and capacity The application of the model is primarily to determine its capability of accurately assessing the effect of complex integrations of systems, and process output mass-flow balancing in preliminary engineering design of large inte-grated processes A layout of part of the OOP simulation model is given in Fig 1.2
Trang 5Fig 1.1 Layout of the RAM analysis model
c) The AIB Blackboard Model
The AIB blackboard model consists of three fundamental stages of analysis for
de-termining the integrity of engineering design, specifically preliminary design pro-cess analysis, detail design plant analysis and commissioning operations analysis The preliminary design process analysis incorporates the essential preliminaries of
design review, such as process definition, performance assessment, process design
evaluation, systems definition, functions analysis, risk assessment and criticality
analysis, linked to an inter-disciplinary collaborative knowledge-based expert sys-tem Similarly, the detail design plant analysis incorporates the essential prelimi-naries of design integrity such as FMEA and plant criticality analysis The applica-tion of the model is fundamentally to establish automated continual design reviews
whereby the integrity of engineering design is determined concurrently throughout the engineering design process Figure 1.3 shows the selection screen of a multi-user interface ‘blackboard’ in collaborative engineering design
Trang 6Fig 1.2 Layout of part of the OOP simulation model
1.2.2 Artificial Intelligence in Engineering Design
Implementation of 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 process This hinges, however, upon
a broader understanding of the principles and philosophy of the use of artificial intelligence (AI) in engineering design, particularly 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 detail 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 design 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 of artificial intelligence seems to have damaged the
credibility of AI and eroded its true contributions and benefits The early advances
Trang 7Fig 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 develop a proper perspective on the relationship between AI
tech-nology and 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
Knowledge engineering is a problem-solving strategy and an approach to
program-ming that characterises a problem principally by the type of knowledge involved
At one end of the spectrum lies conventional engineering design technology based on well-defined, algorithmic knowledge At the other end of the spectrum lies
AI-related engineering design technology based on ill-defined heuristic knowledge.
Trang 8Among the problems that are well suited for knowledge-based systems are design problems, in particular engineering design As engineering knowledge is heteroge-neous in terms of the kinds of problems that it encompasses and the methods used
to solve these, the use of heterogeneous representations is necessary Attempts to characterise engineering knowledge have resulted in the following classification of the properties that are essential in constructing a knowledge-based expert system:
• Knowledge representation,
• Problem-solving strategy, and
• Knowledge abstractions.
b) Engineering Design Expert Systems
The term ‘expert system’ refers to a computer program that is largely a collection of
heuristic rules (rules of thumb) and detailed domain facts that have proven useful in
solving the special problems of some or other technical field Expert systems to date are basically an outgrowth of artificial intelligence, a field that has for many years
been devoted to the study of problem-solving using heuristics, to the construction of symbolic representations of knowledge, to the process of communicating in natural language and to learning from experience
Expertise is often defined to be that body of knowledge that is acquired over many years of experience with a certain class of problem One of the hallmarks
of an expert system is that it is constructed from the interaction of two types of
disciplines: domain experts, or practicing experts in some technical domain, and knowledge engineers, or AI specialists skilled in analysing processes and
problem-solving approaches, and encoding these in a computer system
The best domain expert is one with years, even decades, of practical experience, and the best expert system is one that has been 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, problems requiring knowledge-intensive problem solving, 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-intensive problem 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 solving, 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.
Trang 9c) 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;
• Strategies for searching in the space of alternative designs, and monitoring of
progress towards the targets of the design process;
• Integration of a diverse set of tools, with expertise applied to the problem of
engineering design project planning and control;
• Integration of the various stages of an engineering design project, inclusive of
procurement/installation, construction/fabrication, and commissioning/warranty
by having knowledge bases that can be distributed for wide access in a collabo-rative design environment
d) Research in Expert Systems for Engineering Design
Within the past several years, a number of tools have been developed that allow
a higher-level approach to building expert systems in general, although most still re-quire some programming skill A few provide an integrated knowledge engineering
environment combining features of all of the available AI languages.
These languages (CLIPS, JESS, etc.) are suitable and efficient for use by AI
pro-fessionals A number of others are very specialised to specific problem types, and can be used without programming to build up a knowledge base, including a number
of small tools that run on personal computers (EXSYS, CORVID, etc.) A common
term for the more powerful tools is shell, referring to their origins as specialised expert systems of which the knowledge base has been removed, leaving only a shell
that can perform the essential functions of an expert system, such as
• an inference engine,
• a user interface, and
• a knowledge storage medium.
For engineering design applications, however, good expert system development tools are still being conceptualised and experimented with Some of the most recent
techniques in AI may become the basis for powerful design tools Also, a number
of the elements of the design process fall into the diagnostic–selection category, and
Trang 10these can be tackled with existing expert system shells Many expert systems are now being developed along these limited lines The development of a shell that has 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 heuristic
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, one of the main areas of development is to provide integrative means 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 quality of the result, the effectiveness of the design review process, and the integrity of the final design
A class of AI 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
model is a paradigm that allows for the flexible integration of modular portions of design code into a single problem-solving environment It is a general and simple model that enables the representation of a variety of design disciplines Given its nature, it is prescribed for problem solving in knowledge-intensive domains that use large amounts of diverse, error-full and incomplete knowledge, therefore requir-ing multiple cooperation between knowledge sources in searchrequir-ing a large problem space—which is typical of engineering designs In terms of the type of problems that
it can solve, there is only one major assumption—that the problem-solving activity generates a set of intermediate results that contribute to the final solution