Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design - Part 75 ppt

1.1, the five main objectives that need to be accomplished in pursuit of the goal of the research in this handbook are: • the development of appropriate theory on the integrity of engine

Fig 5.78 Ward back propagation ANN architecture (Schocken 1994)

Fig 5.79 Probabilistic (PNN)

ANN architecture (Schocken

1994)

Fig 5.80 General regression

(GRNN) ANN architecture

(Schocken 1994)

GRNN applications are able to produce continuous valued outputs and respond bet-ter than back propagation in many cases (Fig 5.80)

Unsupervised neural network Kohonen self-organising map—contains an input

and an output layer One neurode is present in the output layer for each category specified by the user Kohonen networks are known to separate data into a specified number of categories (Fig 5.81)

In Sect 5.4, an artificial intelligence-based blackboard model is used to hold

shared information in a general and simple model that allows for the representa-tion of a variety of modelled system behaviours The AIB blackboard system is prescribed for problem-solving in knowledge-intensive domains that require large

Fig 5.81 Kohonen

self-organising map ANN

archi-tecture (Schocken 1994)

amounts of diverse and incomplete knowledge, therefore necessitating multiple co-operation of various knowledge sources

One knowledge source, a neural expert program (Lefebvre et al 2003), is em-bedded in the AIB blackboard for processing of time-varying information, such as non-linear dynamic modelling, time series prediction, and adaptive control of vari-ous engineering design problems

5.4 Application Modelling of Safety and Risk

in Engineering Design

Returning to Sect 1.1, the five main objectives that need to be accomplished in pursuit of the goal of the research in this handbook are:

• the development of appropriate theory on the integrity of engineering design for

use in mathematical and computer models;

• determination of the validity of the developed theory by evaluating several case

studies of engineering designs that have been recently constructed, that are in the process of being constructed or that have yet to be constructed;

• application of mathematical and computer modelling in engineering design

veri-fication;

• determination of the feasibility of a practical application of intelligent computer

automated methodology in engineering design reviews through the development

of the appropriate industrial, simulation and mathematical models

The following models have been developed, each for a specific purpose and with specific expected results, in partly achieving these objectives:

• RAMS analysis model, to validate the developed theory on the determination of

the integrity of engineering design

• Process equipment models (PEMs), for application in dynamic systems

simula-tion modelling to initially determine mass-flow balances for preliminary engi-neering designs of large integrated process systems, and to evaluate and verify process design integrity of complex integrations of systems

• Artificial intelligence-based (AIB) model, in which relatively new artificial

intel-ligence (AI) modelling techniques, such as inclusion of knowledge-based expert systems within a blackboard model, have been applied in the development of

intelligent computer automated methodology for determining the integrity of en-gineering design

The third model, the artificial intelligence-based (AIB) model, will now be

consid-ered in detail in this section

5.4.1 Artificial Intelligence-Based (AIB) Blackboard Model

Artificial intelligence (AI) has been applied to a number of fields of engineering

design Although there are some features that the various design areas share, such

as the need to integrate heuristics with algorithmic numerical procedures, there are also some important differences Each field of engineering seems to recognise the importance of representing declarative concepts, although specific needs vary In process engineering, for example, the hierarchical representation of components with their functional relationships seems to be vital In mechanical engineering, the representation of solid geometric shapes has been thoroughly studied and is viewed

as being crucial to the successful evolution of computer aided design or manufac-turing CAD/CAM systems Artificial intelligence in engineering design can be de-scribed as a discipline that provides a multi-level methodology for knowledge-based

problem-solving systems, in which a knowledge-level specification of the system (and the class of problems it must solve) is mapped into an algorithm-level descrip-tion of an efficient search algorithm for efficiently solving that class of problems The algorithm description is then mapped into program code at the program level,

using one or more programming paradigms (e.g procedural programming,

rule-based programming or object-oriented programming, OOP), or shells (e.g

RAM-ESP), or commercially available sub-systems (e.g CLIPS, JESS or EXSYS) The application of AI to engineering design thus represents a specialisation of software engineering methodology to:

• Design tasks

(specified at the ‘knowledge level’).

• Design process models

(described at the ‘algorithm level’).

• Design programs built from shells

(implemented at the ‘program level’).

Integration of the design process with blackboard models The quality of

engi-neering design using traditional CAD techniques is adversely affected by two fea-tures of the design process

Features of the design process affecting the quality of engineering design are:

• Limited scope in addressing problems that arise in the many stages of the

devel-opment of an engineered installation

• A lack of understanding of the essential processes involved in engineering

de-sign

Both of these are related to systems integration issues The life cycle of an

engi-neered installation can be described by a collection of projects, where each project

involves a coherent set of attributes, such as the design, manufacturing or assem-bling of a system Traditional CAD tools typically address some narrow aspect of the design project, and fail to provide integrated support for the development of an

engineered installation, particularly evaluation of design integrity Essentially, mod-ern engineering design of complex systems requires an approach that allows mul-tiple, diverse program modules, termed knowledge sources, to cooperate in solving complex design problems

The (AIB) blackboard model The artificial intelligence-based (AIB) blackboard

model that has been developed enables the integration of multiple, diverse program

modules into a single problem-solving environment for determining the integrity

of engineering design This AIB blackboard model is a database that is used to hold shared information in a centralised model that allows for the representation

of a variety of modelled system behaviours Given the nature of programming for blackboard systems, it is prescribed for problem-solving in knowledge intensive domains that require large amounts of diverse and incomplete knowledge, therefore requiring multiple cooperation of various knowledge sources in the search of a large problem space

The AIB blackboard model consists of a data structure (the blackboard) contain-ing information (the context) that permits a set of modules (knowledge sources) to interact The blackboard can be seen as a global database or working memory in which distinct representations of knowledge and intermediate results are integrated uniformly It can also be seen as a means of communication among knowledge sources, mediating all of their interactions in a common display, review and per-formance evaluation area The engineering design methodology for the AIB black-board model, presented in the following graphical presentation (Fig 5.82), applies the concept of object-oriented programming

Object-oriented programming (OOP) has two fundamental properties, encapsu-lation and inheritance Encapsuencapsu-lation means that the user (the engineering designer)

can request an action from an object, and the object chooses the correct operator,

as opposed to traditional programming where the user applies operators to operands and must assure that the two are type compatible The second property, namely inheritance, greatly improves the re-usability of code, as opposed to traditional pro-gramming where new functionality often means extensive re-coding

In this way, the AIB blackboard model may be structured so as to represent dif-ferent levels of abstraction and also distinct and possibly overlapping solutions in the design space of complex engineering design problems 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

The AIB blackboard model for engineering design integrity consists of four sections, each section containing six design modules, culminating in a summary design analysis module particular to each specific section (Fig 5.83) The first sec-tion of the AIB blackboard model contains modules or knowledge sources for as-sessing preliminary design (inclusive of conceptual design basics), such as process definition, performance assessment, RAM assessment, design assessment, HazOp analysis, and critical process specifications, including a summary process analysis module The second section contains modules for evaluating detail design, such as systems definition, functions analysis, FMEA, risk evaluation, criticality analysis,

Fig 5.82 AIB blackboard model for engineering design integrity (ICS 2003)

and critical plant specifications, including a summary plant analysis module The third section contains modules related to operations analysis, and the fourth sec-tion contains modules of knowledge-based expert systems relating to the modules

of the three former sections Thus, the expert system module called ‘facts’ relates to process definition, systems definition and operating procedures, etc

Most engineering designs are still carried out manually with input variables based

on expert judgement, prompting considerable incentive to develop model-based

techniques Investigation of safety-related issues in engineering designs can effec-tively be done with discrete event models A process plant’s physical behaviour can

be modelled by state transition systems, where the degree of abstraction is adapted both to the amount of information that is available at a certain design phase, and to the objective of the analysis A qualitative plant description for designing for safety

is sufficient in the early design phases, as indicated in Figs 5.83 to 5.87 However, the verification of supervisory controllers in later design phases requires finer mod-elling such as the development of timed discrete models The procedure of model

refinement and verification is later illustrated by the application of expert systems.

A systematic hierarchical representation of equipment, logically grouped into

systems, sub-systems, assemblies, sub-assemblies and components in a systems breakdown structure (SBS), is illustrated in Fig 5.84.

Fig 5.83 AIB blackboard model with systems modelling option

The systems breakdown structure (SBS) provides visibility of process systems

and their constituent assemblies and components, and allows for safety and risk analysis to be summarised from system level to sub-system, assembly, sub-assembly

and component levels The various levels of the SBS are normally determined by

a framework of criteria established to logically group similar components into sub-assemblies or sub-assemblies, which are then logically grouped into sub-systems or

sys-tems This logical grouping of items at each level of an SBS is done by identifying

the actual physical design configuration of the various items at one level of the

SBS into items of a higher level of the systems hierarchy, and by defining common

operational and physical functions of the items at each level When designing or analysing a system for safety, a method is needed to determine how the variables

are interrelated System hierarchical models based on a structured SBS, as

illus-trated in Fig 5.85, provide formulations of the core concept of a system in order to match the particular modelling perspective—for example, establishing FMEA and criticality analysis in designing for safety

The particular model formalisms that are used depend on the objectives of the modelling requirements and the modelling techniques applied In the case of

schematic design modelling, the formalisms commonly used are functional (what

a system can do), behavioural (describes or predicts the system’s dynamic response)

Fig 5.84 Designing for safety using systems modelling: system and assembly selection

and schematic (an iconic model of the system’s structure and connectivity) Thus,

a schematic design model contains design variables and constraints describing the structural and geometric feature of the design A detail design model typically has variables and constraints representing embodiment, structure and assembly, and dy-namic flow and energy balance information of the process layout Designing for safety begins with a schematic design model, as graphically illustrated in Fig 5.85, and development of a systems hierarchical structure as graphically illustrated in Fig 5.86

The treeview illustrated in the left column of Fig 5.86 enables designers to view

selected equipment (assemblies, sub-assemblies and components) in their cascaded systems hierarchical structure

The equipment and their codes are related according to the following systems breakdown structure (SBS):

• components,

• assemblies,

• systems,

• sections,

• operations,

• plant.

Fig 5.85 Designing for safety using systems modelling

A selection facility in the treeview, alongside the selected component, enables the

designer to directly access the component’s specific technical specifications, or spares bill of materials (BOM)

Equipment technical data illustrated in Fig 5.87 automatically format the tech-nical attributes relevant to each type of equipment that is selected in the design process

The equipment technical data document is structured into three sectors:

• technical data obtained from the technical data worksheet, relevant to the

equip-ment’s physical and rating data, as well as performance measures and perfor-mance operating, and property attributes that are considered during the design process,

• technical specifications obtained from an assessment and evaluation of the

re-quired process and/or system design specifications,

• acquisition data obtained from manufacturer/vendor data sheets, once equipment

technical specifications have been finalised during the detail design phase of the engineering design process

A feature of the systems modelling option in the AIB blackboard model is to

de-termine system failure logic from network diagrams or fault-tree diagrams, through Monte Carlo (MC) simulation.

Fig 5.86 Treeview of systems hierarchical structure

Figure 5.88 illustrates the use of the network diagram in determining potential

system failures in a parallel control valve configuration of a high-integrity protection system (HIPS) Isograph’s AvSimc Availability Simulation Model (Isograph 2001)

has been imbedded in the AIB blackboard for its powerful network diagramming ca-pability, especially in constructing block diagrams The network diagram consists of blocks and nodes connected together in a parallel (and/or series) arrangement The blocks in the network diagram usually represent potential component or sub-system failures, although they may also be used to represent other events such as operator actions, which may affect the reliability of the system under study The nodes in the network diagram are used to position connecting lines and indicate voting arrange-ments The complete system network diagram will consist of either a single node

or block on the left-hand side of the diagram (input node or block) connected via intermediate nodes and blocks to a single node or block on the right-hand side of the diagram (output node or block) A complete system network diagram can have only one input node or block and one output node or block In addition, all the inter-mediate nodes and blocks must be connected The entire system network diagram represents ways in which component and sub-system failures will interact to cause the system to fail

Fig 5.87 Technical data sheets for modelling safety

Monte Carlo simulation is employed to estimate system and sub-system

param-eters such as number of expected failures, unavailability, system capacity, etc The process involves synthesising system performance over a given number of simula-tion runs In effect, each simulasimula-tion run emulates how the system might perform in real life, based on the input data provided by the blackboard system’s knowledge

base The input data can be divided into two categories: a failure logic diagram, and quantitative failure and/or maintenance parameters The logic diagram (either

a fault tree or a network diagram, in this case) informs the knowledge base how component failures interact to cause system failures The failure and maintenance parameters indicate how often components are likely to fail and how quickly they should be restored to service By performing many simulation runs, a statistical pic-ture of the system performance is established Monte Carlo simulation must emulate the chance variations that will affect system performance in real life To do this, the model must generate random numbers that form a uniform distribution Simulation methods are generally employed in reliability studies when deterministic methods are incapable of modelling strong dependencies between failures In addition, sim-ulation can readily assess the reliability behaviour of repairable components with non-constant failure or repair rates