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Knowledge based engineering
approach for subsea pipeline
systems’FFR assessment
A fuzzy expert system
R.M. Chandima Ratnayake
Department of Mechanical and Structural Engineering and Materials Science,
University of Stavanger, Stavanger, Norway
Abstract
Purpose –The purpose of this paper is to focus on developing a knowledge-based engineering (KBE)
approach to recycle the knowledge accrued in an industrial organization for the mitigation of
unwanted events due to human error. The recycling of the accrued knowledge is vital in mitigating the
variance present at different levels of engineering applications, evaluations and assessments in
assuring systems’safety. The approach is illustrated in relation to subsea systems’functional failure
risk (FFR) analysis.
Design/methodology/approach –A fuzzy expert system(FES)-based approach has been proposed to
facilitate FFR assessment and to make knowledge recycling possible via a rule base and membership
functions (MFs). The MFs have been developed based on the experts’knowledge, data, information, and
on their insights into the selected subseasystem. The rule base has been developed to fulfill requirements
and guidelines specified in DNV standard DNV-RP-F116 and NORSOK standard Z-008.
Findings –It is possible to use the FES-based KBE approach to make FFR assessments of the
equipment installed in a subsea system, focussing on potential functional failures and related
consequences. It is possible to integrate the aforementioned approach in an engineering service
provider’s existing structured information management system or in the computerized maintenance
management system (CMMS) available in an asset owner’s industrial organization.
Research limitations/implications –The FES-based KBE approach provides a consistent way to
incorporate actual circumstances at the boundary of the input ranges or at the levels of linguistic data
and risk categories. It minimizes the variations present in the assessments.
Originality/value –The FES-based KBE approach has been demonstrated in relation to the
requirements and guidelines specified in DNV standard DNV-RP-F116 and NORSOK standard Z-008.
The suggested KBE-based FES that has been utilized for FFR assessment allows the relevant
quantitative and qualitative data (or information) related to equipment installed in subsea systems to
be employed in a coherent manner with less variability, while improving the quality of inspection and
maintenance recommendations.
Keywords Knowledge-based systems, Risk management, Fuzzy logic, Oil industry, Maintenance,
Management techniques
Paper type Case study
Introduction
Knowledge-based engineering (KBE) developments focus recycling the knowledge,
which has previously been stored in the minds, charts, diagrams, etc., of the personnel
(i.e. in terms of experience, insights, etc.) who have been extensively involved in
engineering projects (Leake et al., 2014; da Silva et al., 2014; Stokes, 2001). For instance,
risk assessment that has been used to identify and eliminate known or potential failures
in order to enhance the reliability and safety of complex systems requires the recycling of
an extensive amount of engineering knowledge (Gu et al., 2012; Ratnayake, 2014a).
However, KBE has not been adopted in most engineering applications, as currently there
The TQM Journal
Vol. 28 No. 1, 2016
pp. 40-61
© Emerald Group PublishingLimited
1754-2731
DOI 10.1108/TQM-12-2013-0148
Received 6 January 2014
Revised 20 July 2014
Accepted 7 August 2014
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1754-2731.htm
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is no generic approach for structuring engineering knowledge and representing it in such
a way that it is possible to use it in different KBE platforms (Zha and Sriram, 2006).
In this context, KBE is defined as “the use of advanced software techniques to capture
and re-use product and process knowledge in an integrated way”(Stokes, 2001; Leake
et al., 2014). The following actors are mainly involved with KBE approaches: experts
(i.e. those who “are responsible for defining the domain knowledge to be applied in the
KBE system by end-users”–e.g. engineers involved in assessment); knowledge engineers
(i.e. those who “structure and formalize the expert’s knowledge in a consistent and
unambiguous format”and who are “familiar with the field of application”in order to
describe the knowledge); developers (i.e. those who “transform the formalized knowledge
into operational applications”and “write the code for a particular KBE platform”–e.g.
programmers or trained analysts); end-users (i.e. those who will use the final KBE
application to perform a specific assessment –e.g. newly recruited engineers who are
already involved in assessment and evaluations) (Stokes, 2001).
The functional failure risk (FFR) assessments of offshore oil and gas (O&G)
production and process systems are currently performed using established
recommended practices (e.g. DNV-RP-F116 (2009); NORSOK standard Z-008, Z008,
2011), experts’knowledge (i.e. by means of experience, insights, data, and information)
(Seneviratne and Ratnayake, 2012; Ratnayake, 2014a). However, the risk assessments
show significant variation from one to the other due to “uncertainty”that has been
caused by: fuzziness (i.e. “lack of definite or sharp distinctions”); ambiguity (i.e. “one-to-
many relationships”); discord (i.e. “disagreement in choosing among several
alternatives”); and nonspecificity (i.e. “two or more alternatives are left unspecified”)
(Klir and Yuan, 1995). Hence, “expert”or “knowledge based”systems’development is
vital to recycle established knowledge to perform risk assessments that would
normally require a well-experienced human expert (Pillay and Wang, 2003; Eilouti,
2009). However, the aforementioned approach does not seek to replace the human
involvement; instead, the aim is to replace the routine assessment processes for which
the knowledge is well established and understood (Li et al., 2011). For instance, these
assessment processes take significant time (or effort) and yet require very little creative
thought to make further improvements. Hence, the aim of KBE developments is to let
the computers take on the repetitive (or dull) routines to reduce the variability that may
be present in the final assessment due to diverse expertise (Gu et al., 2012).
In addition to the aforementioned, currently there is a serious concern in the O&G
industry that knowledge existing or accrued for assessment processes diminish over the
time as a result of knowledge migration from one organization to another (Ratnayake,
2014b). This is mainly due to the fact that there is no formal mechanism for experienced
engineers in the workforce to pass on much hard-earned knowledge (i.e. in terms of
experience and insights) to the new recruits who are less experienced (Maruta, 2014).
However, KBE is not expected to replace suitably qualified and experienced engineers;
instead, it does provide a mechanism to hold and recycle any available knowledge (Maruta,
2014). Moreover, it demands a suitable means of representing the accrued knowledge
gained from different projects, supporting new recruits and maintaining the quality of
deliverables at an anticipated level with less variability.
The KBE concept routes back to the 1950s (Sandberg, 2003), with the
developments during this time mainly focussing on enabling a system to have its
own intelligence, known as artificial intelligence (AI). The AI techniques are mainly
used to devise adaptive solving strategies that are employed to solve a broad
spectrum of tasks (Pota et al., 2014; Ciarapica and Giacchetta, 2009). However, these
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approaches have been put aside for a while due to the challenge of the computational
complexity vs the time taken for a human to solve it. However, the concept of KBE
focusses on the use of advanced software techniques to capture and recycle the
knowledge in an integrated way and resurrects the possibility of using AI approaches
with a human touch (Stokes, 2001; Lovett et al., 2000). Aside from that, it focusses on
minimizing routine assessment work to about 20 percent, while allowing creative
work to expand to about 80 percent, reducing the overall cycle time (Mason, 2014;
Stokes, 2001). For instance, membership functions (MFs), together with a rule base in
the fuzzy set theory approach, enable the integration and recycling of existing
knowledge that has been accrued over the quantitative and qualitative data, as well
as personnel experience and skills (Pota et al., 2014; Ratnayake, 2013; Ciarapica and
Giacchetta, 2009). Hence, this manuscript illustrates the KBE development via the use
of a fuzzy logic-based consistent approach, making the criticality assessment
of subsea systems align with the guidelines provided in NORSOK standard Z-008
(Z008, 2011). The approach is developed to perform an initial screening of subsea
equipment criticality assessment. Subsequently, it is possible to use the results
of such assessments for carrying out detailed assessment.
Background for the KBE development
The safety regulations applying to petroleum activities (i.e. O&G) on the Norwegian
Continental Shelf suggest classifying systems and equipment on production and/or process
systems in relation to the health, safety and environmental (HSE) consequences of potential
functional failures (Ptil, 2011). In this context, the responsibility for carrying out the
consequence classification (i.e. based on the potential functional failures in O&G
production/process equipment that can lead to serious consequences) assessment lies with
the particular petroleum production and/or process facility owner’s industrial organization
(Ratnayake, 2014a; Wang, 2001). In essence, the asset owner’s industrial organization
outsources the assessment work to an engineering contractor organization which has a
track record of providing expertise for performing such assessments (Ratnayake, 2012a).
Usually, the assessment is performed with the help of existing data (e.g. OREDA (2009) ‒
Offshore REliability Data) as well as experts’experience, knowledge, and insights.
The assessment focusses on identifying the various fault modes with associated failure
causes and failure mechanisms, estimating the probability of failure (PoF) for the individual
fault mode, and assessing the potential consequence of failure (CoF).
Based on the PoF and CoF of potential failures, the initial screening (or
classification) of the facilities’systems and equipment is performed (Ratnayake, 2014b;
Harms-Ringdahl, 2003; Hale et al., 1997; Ciarapica and Giacchetta, 2009). In this context,
“a failure is the termination of the ability of an item to perform its required function”
(DNV-RP-F116, 2009). The “failure”is “an event affecting a component or system and
causing one or both of the following events: loss of component or system function;
or deterioration of functional capability to such an extent that the safety of the
installation, personnel or environment is significantly reduced”(DNV-OS-F101, 2012).
The main focus of the initial screening (or classification) is to use it as a basis for
selecting maintenance activities and maintenance frequencies, for prioritizing between
different maintenance activities, for evaluating the need for resources (e.g. remotely
operated vehicles (ROVs), nondestructive evaluation (NDE) tools, etc.) and finally,
to carry out a detailed assessment (i.e. to recognize the need for inspection,
modification, repair, etc.) for the equipment with high-risk ranking. Figure 1 illustrates
the overall process involved in subsea systems’FFR assessment.
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However, current practices reveal that the inconsistency (or variance) among evaluations
(e.g. in assessment, planning, etc.) is significantly high (Ratnayake, 2014b). Hence, it is vital
to develop a consistent approach to improve systems’safety and minimize waste (in terms
of quality loss, time, etc.) (Diana, 1995). Standards such as DNV-RP-F116 (2009), DNV-OS-
F101 (2012) and NORSOK standard Z-008 provide requirements and guidelines for
risk-based inspection and risk-based maintenance (RBM). The standards provide
directions for performing a classification of consequences due to potential failures and
alternatively, revising (or recommending) necessary maintenance activities for plant
systems and equipment in the Norwegian petroleum industry (Z-008, 2011). In particular,
NORSOK Z-008 covers the aforementioned in the design phase, in the preparation for
operations, and in the operational phase of offshore topside, subsea production and O&G
terminals. The standard uses risk assessment as the guiding principle for maintenance
decisions. It is suggested that the RBM decisions are carried out against defined criteria
and that the criteria are in accordance with the selected asset owner’s overall policy for the
minimization of HSE, production, and cost-related challenges (Ptil, 2011). The current
practice is to develop a risk matrix (i.e. aligned with NORSOK standard Z-008 guidelines)
along with possible ranges or linguistic terms and then to perform risk assessments.
As there is no means to study the boundary of each range or level of a linguistic term,
engineering practice reveals final risk assessments to be suboptimal (Ratnayake, 2014a).
Hence, it is vital to introduce KBE approaches to the existing evaluation processes
(Chapman and Pinfold, 1999).
Data, information, experience, knowledge, and insights
Retrieve technical
hierarchy from the
existing CMMS
FFR assessment (DNV-RP-F116 and NOSOK Z- 008)
Screening
FMECA on selected equipment/ systems
Decision model
CBM
TBM
Redesign/modify to
reach anticipated
FFC level
CM
Low
Refer to benchmarking practices, recommendations, and guidelines from suppliers, regulatory
authorities, other industrial organizations, etc.
Analyze for continuous
improvement:
(i.e. review: current
practices, resource
utilization, regularity
requirements, etc.)
Medium
HighVery high
Collect and record: data,
information, experience
and insights record
(i.e. creation of knowledge
base)
Record the results in an
asset owners’ CMMS
(i.e. returned back to
technical hierarchy from
the functional hierarchy)
Implement maintenance
activities
(i.e. effective and efficient
execution of existing
capabilities)
Existing assets Newly built assets
Develop functional hierarchy (ref. NOSOK Z- 008)
Develop technical
hierarchy
In- service
inspection
Condition
monitoring Figure 1.
Overall process
involved in
subsea systems’
FFR assessment
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The role of human errors vs equipment failures
The Piper Alpha disaster almost 26 years ago and the Deepwater Horizon catastrophe
four years ago reveal that there is no doubt that safety assessment and systems remain
top priority (Ratnayake, 2012b; Ratnayake and Markeset, 2012). As the continuous
technology evolution makes systems and operations more sophisticated, dangerous
and complex, it is vital to cut back on the weakest link (i.e. people) in the safety chain
(Hale, 2014). The more autonomation (i.e. letting machines work harmoniously with
their operators by giving them the “human touch”) there is in assessments (e.g. the use
of KBE approaches) and systems operations, the more help there is to cut back the
human involvement, eliminating hazards, while letting people remain involved in
safety systems and in running the process (Aziz and Hafez, 2013; Hale, 2014).
The recent comprehensive investigations into unwanted incidents revealed that:
minimal awareness, equipment errors, human errors, risk management, organizational
weaknesses, working culture at the site, inspection and maintenance, general health
and safety assessments, etc. led the particular operational asset(s) to a catastrophic
incident (Ratnayake, 2011). Moreover, Lardner and Fleming (1999) revealed that
as a rule of thumb, 80 percent of large-scale accidents and disasters are due to a
combination of both human and organizational causes, while only 20 percent of
accidents are due to technical causes. DOE Standard (2009) reveals that only about
30 percent of accidents are contributed to by individual mistakes and about 70 percent
of them have been caused by organizational weaknesses. Hence, considering the
combination of the two aforementioned findings, it is possible to estimate that more
than half (56 percent) of unwanted events are caused by organizational weaknesses
leading to human errors, and only a quarter (24 percent) of unwanted events are caused
by individual mistakes leading to human errors (see Figure 2).
Hence, organizations need to take measures to prevent accidents rather than merely
focus on technical challenges related to failures. For instance, the investigation report
about the Hercules military flight which crashed onto a mountainside in northern
Sweden, killing all five officers on board, revealed that “poor routines in planning,”
20%
Equipment
failures
24%
Individual
mistakes
56%
Organizational
weaknesses
Source: Ratnayake (2009)
Figure 2.
Percentage
contribution of
organizational
weaknesses,
equipment failures
and individual
mistakes to
unwanted events
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staff being “relatively new on the job and inexperienced,”“letting employees with
limited experience have responsibility for considerable traffic”are some of the facts
which led to the accident (Newsinenglish, 2013). The current O&G industry also suffers
from frequent knowledge migration, leaving new inexperienced personnel to carry out
assessment and planning and make recommendations, etc. Hence, it is vital to retain
the experts’knowledge as much as possible within an organization to assure their
processes are at the expected level of system function. In this context, it is possible to
mitigate the effect of knowledge migration to a certain extent by incorporating KBE.
Figure 3 illustrates how KBE supports continuous improvement (i.e. over the
improvements made in an isolated fashion).
If an expert leaves an organization, the concept of KBE development enables his/her
knowledge to be incorporated systematically into a mathematical model, which can
effectively and efficiently be implemented with the help of software development.
The developed software together with the suggested model can later be integrated with
an existing structured data management system. The aforementioned approach
supports performing inspection and maintenance assessment with less variability
(i.e. with less variation from person to person), whether the personnel involved
in the assessment are newly recruited or experienced.
Industrial challenge
Standards such as DNV-RP-F116 (2009), DNV-OS-F101 (2012) and NORSOK standard
Z-008 provide requirements and guidelines for constructing a tailor-made risk matrix
(i.e. an O&G assets owner has freedom to adapt the guidelines to fit into the
organizational risk philosophy while satisfying the minimum requirements specified in
the standard(s)) to carry out FFR assessment. For instance, Table I illustrates the
DNV-RP-F116 (2009) approach.
However, when the assessment of FFR due to potential failures is carried out, there
is no formal mechanism to incorporate data and information at the boundaries of the
risk categories (i.e. alternatively at the boundaries of the ranges and levels of linguistic
data). Figure 4 illustrates the general work process, which outlines systematically the
breakdown of plant systems into suitable items for FFR assessment.
Threshold level
System function
Improvements in an
isolated fashion
Continuous improvement (with KBE)
Anticipated level
Time
New At Present Future
Product development, modifications, etc.
Change and /or relaxation of procedures, standards,
etc.
Lack of systems thinking, change management, awareness
of stakeholder requirements, etc.
Changes in product complexity, operating and environmental
conditions, customer requirements, etc.
Lack of competence, system integration, knowledge recycling, etc. Figure 3.
System function vs
time: role of KBE
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Major difficulties lie especially in classifying consequences along the boundary from
high (H) to medium (M) and from medium to low (L), as there are no means to
incorporate real data (qualitative or quantitative). For instance, along a boundary,
the sudden jumps of risk classification hinder the actual level of consequence, which
leads to suboptimal RBM decisions. Figure 5 illustrates an example of how equipment
in a main function is assigned to standard sub-functions.
Essentially, different items of equipment or machinery (i.e. called tags) are evaluated
to assess possible FFR (IEC 61508, 2000). The FFR assessment of each item of
equipment in systems and sub-systems supports prioritizing the maintenance
requirements (e.g. interval, spare-parts, etc.). In this context, equipment is designated
with a “tag.”In essence, the tag coding (or tagging) has been used to “equip an item
function with a label that gives it a unique identification”(Z-DP-002, 1996). Each “tag”
is evaluated based on the consequence categories and possible functional failure
frequency using a tailor-made matrix (as illustrated in Table I).
The data and information for performing FFR assessments are usually gathered
via expert knowledge, documentation (e.g. piping and instrumentation diagram,
process flow diagram, historical data, vendor’s recommendations, etc.), guidelines
(e.g. DNV-RP-F116, DNV-OS-F101, NORSOK Z-008, ISO 14224, API RP 14C, etc.)
and regulatory requirements (e.g. Petroleum Safety Authority (PSA) activity
regulations sections 45, 46, 47 and 48), when establishing maintenance programs for
new plants or updating the existing maintenance programs (Ptil, 2011). All the
aforementioned are equally important to an O&G asset owner’s organization as well
as to the engineering service provider organization in which the FFR assessment has
been performed.
Basically, input ranges to perform FFR assessments are established in a form of a
matrix (i.e. also referred to as a risk matrix). In an FFR assessment, the common
practice is to use qualitative data or discrete scales of input ranges. The aforementioned
Table I.
Risk matrix
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OutputMapping functions to
equipment
Consequence classificationIdentify main- and
sub-functions
Basic design documentation
Analysis results from:
• Safety critical elements
(barriers)
• RBI
• Plant and systems
availability studies
Develop/retrieve
technical hierarchy
including
documentation
Identify main
functions
Identify sub- functions
Assign consequences
and redundancy level
to main- and
sub-functions
Mapping of
equipment (tag) to
sub-functions
Common risk
management system
and priority of actions
Result per equipment
(tag)
• Safety function
• Leak HSE
• HSE consequence
• Production loss
consequence
• Other consequences
• Redundancy
Establish maintenance
programme
Source: Adapted from Z- 008 (2011)
Figure 4.
FFR work process
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P3
Pump
T1
Tank
PM3
Line 1- OL
Line 2 - OL Line 3 - OL
Line 4 - OL
Line 5 - OL
Line 6 - OL
XV 2
LE 4
LX 4
LG 3
LT 1
LX 4
LV 4 MV 5 C2
Strainer
PSV 6
XV 7
Containment:
oil liquid
Shut down
equipment
Controlling
Containment:
oil liquid
Pressure relief
Controlling
Controlling Manual shut-off Main task
Controlling
Monitoring
Local indication
Containment:
oil liquid
Containment:
oil liquid
Shut down
equipment
Containment:
oil liquid
Source: Adapted from Z- 008 (2011)
Figure 5.
Illustration of
equipment main
function(s) and
sub-functions
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cause uncertainty, especially, when an assessment has to be made based on an input value
(qualitative or quantitative) at the boundary of a range (Ratnayake, 2014b). This causes
significant variation among the assessments made by personnel who have diverse levels
of experience. This has further been exacerbated when the assessments are compelled
to be made on an ad hoc basis due to the lack of a consistent mechanism. Hence, it is vital
to develop KBE approaches to recycle the anticipated knowledge in a consistent manner.
Methodology
The use of FFR assessment in RBM planning in a subsea system is employed to
illustrate the fuzzy expert system (FES)-based KBE approach. The FFR assessment
guidelines in DNV-RP-F116 and NORSOK Z-008 have been selected to illustrate
possible knowledge recycling in assuring subsea systems’functional performance
(DNV-RP-F116, 2009; Z-008, 2011). The guidelines regarding the activities’
regulations, section 46 of the PSA Norway, request the use of NORSOK standard
Z-008 for FFR assessments in mitigating the challenges in health, working
environment and safety of the O&G operational (or newly built) assets (Ptil, 2012).
However, as NORSOK standard Z-008 has mostly focussed on topside rotating
equipment, DNV-RP-F116 standard has been utilized in conjunction with it. In this
context, NORSOK standard Z-008 provides requirements and guidelines for
performing RBM and FFR assessments for plant systems and equipment in the
Norwegian petroleum industry (Z-008, 2011). Moreover, DNV-RP-F116 provides
guidelines for operating subsea pipeline systems safely and without “loss of
component or system function”in such a way that “deterioration of functional
capability to such extent that the safety of the installation, personnel or environment
is significantly reduced”is avoided (DNV-RP-F116, 2009). Hence, DNV-RP-F116
guidelines have been utilized to develop the risk matrix which has later been used as
the rule base. The knowledge base has been developed with the help of MFs and
assessment rules (or simply a rule base).
FES
Recently, Pillay and Wang (2003) proposed modified knowledge (i.e. using experts’
knowledge) based failure mode and effects analysis for estimating the risk. In this
context, FESs, which are based on fuzzy logic, play a significant role. The fuzzy logic
provides a form of a logic in which the variables can have degrees of truthfulness or
falsehood represented by a range of values between 1 (true) and 0 (false), enabling the
outcome of an operation to be expressed as a probability rather than as a certainty
(Bai et al., 2014). Consequently, fuzzy logic allows the development of knowledge-based
systems by descriptive or qualitative representation of expressions such as “very low”
or “very high,”while incorporating symbolic statements that are more natural and
intuitive than mathematical equations (Castro-Schez et al., 2013). Hence, it is possible to
use the direct opinions of multiple experts (i.e. based on a probabilistic interpretation of
MFs) for aggregating the opinions of individual experts.
An FES consists of a rule base and MFs. The rule base comprises a collection of
fuzzy IF–THEN rules, which are utilized by the fuzzy inference engine to determine
a mapping from fuzzy sets in the input universe of discourse U⊂R
n
to fuzzy sets in
the output universe of discourse V⊂R, based on fuzzy logic principles. The fuzzy
IF–THEN rules have the form as follows:
R1ðÞ:IF x1is F1
1and xnis F1
nTHEN yis G1(1)
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where Fj
iand G
j
are fuzzy sets, x¼(x
1
,x
2
,…,x
n
)
T
ϵUand yϵVare input and output
linguistic variables which belong to the input and output universes, respectively, and
j¼1, 2, …,m. The practical experience reveals that these fuzzy IF–THEN rules
provide a convenient framework to incorporate human experts’knowledge.
In Equation (1) each fuzzy IF–THEN rule defines fuzzy set Fj
1,Fj
2…,Fj
n⩾G
j
for
i¼1, 2, …,n, in the product space U×V. Experts’opinions and data/information
retrieved from different sources are taken into the mathematical model using the
aforementioned rules. The main focus is to enhance the discriminating power in
the RBM decision making process while associating the uncertainties related to the
linguistic variables to the degree of criticality at the boundaries such as high to
medium, medium to low, and so on. The rules also allow quantitative (e.g. personal
safety (PS), PoF, environmental degradation (ED), etc.), qualitative and judgmental data
(e.g. personnel safety) to be integrated in a uniform manner (Bowles and Pelaez, 1995;
Guimaraes and Lapa, 2004).
In order to use an FES in engineering systems it is necessary to add a fuzzifier to the
input and a defuzzifier to the output of the FES. The fuzzifier maps crisp points in Uto
fuzzy sets in U, and the defuzzifier maps fuzzy sets in Vto crisp points in V. The fuzzy
rule base and fuzzy inference engine are the same as those in the pure fuzzy
logic system. In 1975, Mamdani built one of the first fuzzy systems which used a set of
fuzzy rules supplied by experienced human operators to control a steam engine
and boiler combination (Mamdani and Assilian, 1975). To date, the Mamdani approach
has been successfully applied to a variety of industrial processes and consumer
products (Wang, 1993).
PS, ED, production loss (PL), cost of subsea intervention (i.e. subsea invention with
ROVs, existing drilling units or dedicated subsea intervention vessels), maintenance
and repair activities (CSIM&R), and PoF have been selected as the input variables and
FFR as the output variable. Figure 6 illustrates the overall view of the proposed fuzzy
criticality assessment system (Ratnayake, 2014a).
The inherent challenges related to risk assessments are as follows: “uncertainty”that
has been caused by: fuzziness (i.e. “lack of definite or sharp distinctions”due to “vagueness,
cloudiness, haziness, unclearness, indistinctness, and sharpness”); ambiguity (i.e. “one to
many relationships”); discord (i.e. “disagreement in choosing among several alternatives”
due to “dissonance, incongruity, discrepancy, and conflict”) and nonspecificity (i.e. “two or
more alternatives are left unspecified”due to “variety, generality, diversity, equivocation,
and imprecision”) (Klir and Yuan, 1995). However, fuzzification as well as the development
of MFs and a rule base with the help of experts’knowledge enables the aforementioned to
be mitigated to a significant level.
The input and output variables shall consist of quantitative, qualitative and
judgmental (i.e. linguistic) data. Using an appropriate MF, the user has “more
confidence”that the input parameter lies in the center of the interval than at the edges.
In this study, the author has incorporated Gaussian MFs (Tay and Lim, 2008), which
are defined by Equation (2):
Gaussian x;c;sðÞ¼excðÞ
2
2s2(2)
where crepresents the center and σdetermines the width of the MFs. To model the
MFs, the Gaussian combination MF (GCMF) (i.e. “gauss2mf”), which is available in
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Fuzzy
conclusions
Fuzzy overall
level of
consequence
Experts’ knowledge, data, information, regulatory guidelines, recommended
practices and Insights
Fuzzification
of input
crisp
variables
Evaluation
of rules
Defuzzification
to crisp output
variables
Fuzzy inference system
Fuzzy MTBF
Generation of
input
membership
functions
Generation of
rule base
Generation of
output
membership
functions
Fuzzy
outputs
FFR level
Fuzzy
inputs
Run - time calculations
Fuzzy expert system
Crisp inputs
Crisp (or
qualitative)
output
Uncertainity
Fuzziness
Ambiguity
Nonspecificity
Discord
PoF
CSIM&R
PL
ED
PS
Figure 6.
KBE development:
fuzzy FFR
assessment system
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MATLAB (R2012b), has been utilized (Mathworks, 2014). The function “gauss2mf”is a
combination of two parameters (i.e. (c,σ)) indicated in Equation (2). It follows the
following syntax (Mathworks, 2014):
y¼gauss2mf x;s1c1s2c2
½
(3)
The first part of the function of the GCMF is specified by σ
1
and c
1
which determines the
shape of the left-most curve. The second part of the GCMF, specified by σ
2
and c
2
,
determines the shape of the right-most curve. Whenever c
1
⩽c
2
,the“gauss2mf ”function
reaches a maximum value of 1. Otherwise, the maximum value is less than 1. The order of
the parameters is as follows: (σ
1
c
1
σ
2
c
2
) (Mathworks, 2014). Moreover, the other
parameters of the FES that have been selected for the current analysis are as follows:
“AND”operator with “minimum,”“OR”operator with “maximum,”“Implication”with
“minimum,”“Aggregation”with “maximum”and “Defuzzification”with “centroid”
algorithm. A fuzzy rule base has been developed using the table-look-up approach
(see Table III) to align with guidelines provided in DNV-RP-F116 (2009) and NORSOK
standard Z-008 (2011). The toolbox simulator tool of MATLAB (R2012b) has been utilized
to execute the suggested FES (MATLAB, 2012).
Case study, data collection, modeling, assessment and results
An illustrative case study was carried out in collaboration with an engineering
contractor company which provides maintenance support services to an operator
company. Hence, the existing RBM assessment process has been selected to illustrate
the risk assessment approach.
MF selection
The consequence (i.e. CoF) and probability (i.e. PoF) of a functional failure have been
selected as input to the FES. The FFR has been assigned as the output. There are three
factors under each functional failure consequence rank. The highest value (among the
factors) of the consequence due to a particular functional failure mode is selected for
assessing the gravity of the consequence of functional failure. However, in the current
case study, if two factors have been assessed to be equal in level of consequence,
the industrial organization uses the following hierarchy: (1). PS; (4). ED; (3). PL; (2).
CSIM&R. The intervals, corresponding membership values and finally MFs (i.e. for
Gaussian MFs: “cand σ”) were established based on experts’knowledge (i.e. based on
experience and insights), company documentation, historical data, literature and the
author’s own experience.
Data collection
The CSIM&R activities are inherently higher for subsea systems than is the case for
topside systems. The cost is often driven by the duration of the time taken to fulfill the
challenge and the need of a dedicated subsea intervention vessel or rig. In this context,
the sophistication and the size of the vessel or rig also play a significant role. Hence,
based on the level of intervention, CoF has been categorized in relation to the cost of the
type of intervention needed. Also, it is possible the impact of subsea systems’failure
to be high on production, depending on the geographical location (e.g. North Sea or
Barents Sea) as a result of the time taken to mobilize intervention vessels and the
sophistication required for the ROV and related inspections in order to plan and
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execute intervention activities. As a rule of thumb, most interventions related to subsea
systems can take approximately 12-19 hours or more (Zijderveld et al., 2012).
Firstly, the ranges, ranks (i.e. for PS) and corresponding consequence severity levels
have been estimated with the help of the asset owner’s documentation, standards, asset
owner’s previous findings, data from similar kinds of applications (e.g. OREDA), and
experienced personnel who have had extensive experience in subsea systems’FFR
assessment and establishing or updating a maintenance program. The subsea
intervention activities depend on the various depth ranges (e.g. shallow: (0-500 m); deep:
(500-1,500 m); ultra-deep: W1,500 m) and level of repair required (Zijderveld et al.,
2012). In essence, subsea intervention is required to execute the inspection and
maintenance activities, when production is interrupted, and to increase the extraction
rate (Zijderveld et al., 2012). Moreover, Infield’s (2009) report revealed that the demand
on vessel days in relation to subsea intervention has a steady increase with medium to
heavy interventions which are roughly accountable for half of the total subsea intervention
requirements. Taking all the aforementioned factors into account, a tailor-made risk matrix
has been developed considering the criteria indicated in DNV-RP-F116 (2009), while
aligning with NORSOK Z-008 (2011) requirements. Table III illustrates risk ranking and
inspection frequency in relation to different risk categories. Table IV illustrates ranks,
linguistic terms, levels and ranges assigned for possible CoF (i.e. due to functional failures)
and PoF (Tables II and III).
Modeling
The input variables PoF together with one of the CoFs (i.e. CSIM&R) have been utilized
to illustrate the methodology. Essentially, CSIM&R plays significant role in subsea
maintenance operations and consequently in making FFR assessments. Table IV
illustrates the parameters of each Gaussian MF (i.e. for ED, MTBF and FFR).
Fuzzification is vital in analyzing the inputs (i.e. ranges estimated for CoFs and PoF)
and outputs (i.e. FFR) close to and beyond the boundaries of different levels in making
optimal subsea systems’FFR assessments. It enables suboptimal inspection and
maintenance recommendations to be minimized. However, in this context, the asset
owner’s organizational risk philosophy influences the defining of ranges and possible
membership values along the different ranges (i.e. CoF and PoF). To establish an MF
plot, the author’s own experience, experts’views (i.e. how they perceive and experience
the influence of different systems and connected equipment on a functional failure), as
well as data and information from the case study asset owner’s organization and other
existing sources (e.g. OREDA) have been utilized. In this case, it means that the way in
which equipment and different instruments are physically connected or affect each
Table II.
Risk ranking
and inspection
frequencies in
relation to different
risk categories
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other’s operation, PoF and CoF, have been taken into consideration. Figure 7 illustrates
the MF plots of CSIM&R.
Figure 8 illustrates MF plots of PoF.
Figure 9 illustrates MFs for possible FFR scenarios and corresponding ranks.
Table III.
Tailor-made rule
base for FFR
assessment
VH H M L VL
Input variable
CIMA&R (0.125, 1.000,
0.125, 1.500)
(0.425, 0.653,
0.125, 0.653)
(0.325, −0.35,
0.325, −0.35)
(0.2, −1.35,
0.225, −1.35)
(0.2, −2.5,
0.2, −2.0)
PoF (0.100, −2.000,
0.10, −1.500)
(0.300, −2.350,
0.100, −2.350)
(0.350, −3.350,
0.350, −3.350)
(0.175, −4.450,
0.350, −4.450)
(0.125, −5.500,
0.125, −5.000)
Output variable
FFR (2.250, 25.000,
2.250, 25.600)
(1.500, 17.500,
2.250, 17.500)
(1.250, 13.500,
1.500, 13.500)
(1.500, 8.000,
1.750, 8.000)
(2.500, 0.000
2.500, 1.000)
Table IV.
Gaussian MF
parameters for input
and output variables
1
0.5
0
–2.5 –2 –1.5 –1 –0.5 0 0.5 1 1.5
HVH
M
L
VL
Input variable: log10 (CSIM&R)
Figure 7.
MF plots of CSIM&R
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Figure 10 illustrates a rule view and example calculation of the FFR rank for selected
subsea equipment (tag). The calculation has been carried out for PoF ¼0.00316 and
CSIM&R ¼3.16 million euro. The suggested FES-based KBE approach calculated the
FFR rank to be 17.4.
The corresponding risk level of the potential failure(s) is H (using the MFs in
Figure 9). The aforementioned linguistic value along with inspection and maintenance
recommendation (i.e. yearly inspection is necessary (see Table IV)) would be recorded in
the final assessment report and, finally, the computer maintenance management
system (CMMS) available in the asset owner’s organization is updated to include the
necessary RBM activities. Also, these analyses support the performance of reliability
centered maintenance assessment at a later stage of a subsea system’s life cycle.
Table V illustrates the summary of overall FFR assessment and corresponding
inspection and maintenance recommendations.
It is possible to perform similar analyses for different equipment (or tags) in
a subsea system based on relevant CoF and PoF combinations. The final FFR for a
certain piece of equipment (or tag) is the highest FFR (i.e. FFR ¼f(PoF, CoF)) that has
been calculated among all possible combinations of PoF and relevant CoFs.
Discussion
Once the MFs and rules for FFR assessments have been established, then it is possible
to perform the assessments in the same way based on the relevant PoFs and CoFs. The
variation due to personnel experience is minimized, as the same MFs and rules are used
in a selected system. This alternatively improves the quality of the FFR assessments
and supports the consistency of the maintenance recommendations. As a result, the
1
0.5
0
–5.5 –5 –4.5 –4 –3.5
Input variable: log10 (PoF)
–3 –2.5 –2 –1.5
HVH
MLVL
Figure 8.
MF plots of PoF
1
0.5
0
510 15 20 25
HVHMLVL
Output variable: rank of FFR
Figure 9.
MF plots of FFR
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variation among different maintenance programs will be minimized. The possibility of
mapping FFR three dimensionally (3D) with respect to two input variables (i.e. FFR
vs PoF and one of the CoFs) enables a sensitivity analysis to be carried out.
For instance, Figure 11 illustrates a 3D plot of FFR vs PoF and CSIM&R.
The 3D plot also enables an assessment of the consistency of the rules used for the
assessments by examining a plot of the FFR surface over the possible combinations
of the input variables. For instance, Figure 10 reveals that there are no significant
inconsistencies, as there are no evident abrupt changes in the FFR for a small change in
the PoF or CSIM&R. Similarly, it is also possible to model other combinations of
recycling the accrued knowledge. Although Gaussian MFs have been employed in the
current study, the use of triangular MFs has also been reported in numerous studies
(see Pedrycz, 1994; Klim, 2004).
CSIM&R = 0.5 PoF = –2.5
1
2
3
4
5
6
7
8
10
9
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
–2.5 1.5 –5.5 –1.5
FFR = 17.4
125
Note: FFR rank is 17.4 for PoF = 0.00316 and CSIM&R =3.16 million euro
Figure 10.
A rule view and an
assessment result
Subsea pipeline
equipment
(tag number) PoF
CoF
(CSIM&R)
(million euro)
Calculated risk
rank
Risk
level Recommendation
8012AB 0.00316 3.16 17.4 H Yearly inspection
Table V.
Use of FFR
assessments for
making inspection
and maintenance
recommendations
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Conclusion
This manuscript employs a software tools-oriented KBE approach that has
been developed to recycle knowledge via an FES. The importance of knowledge
recycling has been explained in relation to FFR assessment. After that, the
possibility of implementing such recycling is illustrated with the help of software
tool Matlab (2012) (i.e. fuzzification via MFs, development of rules to perform
FFR assessment and defuzzification to get the results). An illustrative case has
been performed in relation to the FFR assessment of subsea pipeline systems. The
suggested KBE approach minimizes the variation (i.e. by minimizing uncertainties
that may cause assessments to be performed) and waste (i.e. in terms of time and
other resources) in subsea systems’FFR assessment and making recommendations.
It also enables the weakest link (i.e. human involvement) in safety assessments
to be reduced while allowing “human touch”for further improvements with relevant
experts. In particular, the aforementioned variation, waste and suboptimal
assessments occur as a result of the inexperienced and unsuspecting personnel
who may be involved in FFR assessments. Moreover, it is also possible to integrate
the suggested approach in an existing structured information management system
in an engineering services providing organization or in a CMMS in an asset owner’s
industrial organization.
Further research should be carried out to investigate the possibility of a dynamic
approach to MFs’development to incorporate the conditions (or parameters) that
should change over time in operating systems.
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Corresponding author
Dr R.M. Chandima Ratnayake can be contacted at: chandima.ratnayake@uis.no
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