Vibration induced fatigue (VIF) failure of topside piping is one of the most common causes of the hydrocarbon release on offshore oil and gas platforms operating in the North Sea region. An effective inspection plan for the identification of fatigue critical piping locations has the potential to minimize the hydrocarbon release. One of the primary challenges in preparation of inspection program for offshore piping is to identify the fatigue critical piping locations. At present, the three-staged risk assessment process (RAP) given in the Energy Institute (EI) guidelines is used by inspection engineers to determine the likelihood of failure (LoF) of process piping due to VIF. Since the RAP is afflicted by certain drawbacks, this paper presents an alternative risk assessment approach (RAA) to RAP for identification and prioritization of fatigue critical piping locations. The proposed RAA consists of two stages. The first stage involves a qualitative risk assessment using fuzzy-analytical hierarchy process (FAHP) methodology to identify fatigue critical systems (and the most dominant excitation mechanism) and is briefly discussed in the paper. The fatigue critical system identified during stage 1 of RAA undergoes further assessment in the second stage of the RAA. This stage employs a fuzzy-logic method to determine the LoF of the mainline piping. The outcome of the proposed RAA is the categorization of mainline piping, into high, medium, or low risk grouping. The mainline piping in the high-risk category is thereby prioritized for inspection. An illustrative case study demonstrating the usability of the proposed RAA is presented.

References

References
1.
Keprate
,
A.
, and
Ratnayake
,
R. M. C.
,
2016
, “
Enhancing Offshore Process Safety by Selecting Fatigue Critical Piping Locations for Inspection Using Fuzzy-AHP Based Approach
,”
Process Saf. Environ. Prot.
,
102
, pp.
71
84
.
2.
EI
,
2007
,
Guidelines for the Avoidance of Vibration Induced Fatigue Failure in Process Pipework
,
The Energy Institute
,
London
.
3.
EI
,
2013
,
Guidelines for the Design, Installation and Management of Small Bore Tubing Assemblies
,
The Energy Institute
,
London
.
4.
Keprate
,
A.
, and
Ratnayake
,
R. M. C.
,
2016
, “
Handling Uncertainty in the Remnant Fatigue Life Assessment of Offshore Process Pipework
,”
ASME
Paper No. IMECE2016-65504.
5.
Khan
,
F.
,
Thodi
,
P.
,
Imtiaz
,
S.
, and
Abbassi
,
R.
,
2016
, “
Real-Time Monitoring and Management of Offshore Process System Integrity
,”
Curr. Opin. Chem. Eng.
,
14
, pp.
61
71
.
6.
DNV
,
2010
, “
Risk Based Inspection of Offshore Topsides Static Mechanical Equipment
,” Det Norske Veritas, Høvik, Norway, Standard No.
DNV-RP-G101
.
7.
Chang
,
M. K.
,
Chang
,
R. R.
,
Shu
,
C. M.
, and
Lin
,
K. N.
,
2005
, “
Application of Risk Based Inspection in Refinery and Processing Piping
,”
J. Loss Prev. Process Ind.
,
18
(4–6), pp.
397
402
.
8.
Keprate
,
A.
,
Ratnayake
,
R. M. C.
, and
Sankararaman
,
S.
,
2017
, “
Minimizing Hydrocarbon Release From Offshore Piping by Performing Probabilistic Fatigue Life Assessment
,”
Process Saf. Environ. Prot.
,
106
, pp.
34
51
.
9.
Mamdani
,
E. H.
, and
Assilian
,
S.
,
1975
, “
An Experiment in Linguistic Synthesis With a Fuzzy Logic Controller
,”
Int. J. Man-Mach. Stud.
,
7
(
1
), pp.
1
13
.
10.
Swindell
,
R.
,
2003
, “
Vibration Fatigue in Process Pipework: A Risk Based Assessment Methodology
,”
Bureau Veritas
, Southampton, UK.
11.
Sii
,
H. S.
,
Ruxton
,
T.
, and
Wang
,
J.
,
2001
, “
A Fuzzy Logic Based Approach to Qualitative Safety Modelling for Marine Systems
,”
Reliab. Eng. Syst. Saf.
,
73
(
1
), pp.
19
34
.
12.
Singh
,
M.
, and
Markeset
,
T.
,
2009
, “
A Methodology for Risk Based Inspection Planning of Oil and Gas Pipes Based on Fuzzy Logic Framework
,”
Eng. Failure Anal.
,
16
(
7
), pp.
2098
2113
.
13.
Ratnayake
,
R. M. C.
,
2014
, “
Application of a Fuzzy Inference System for Function Failure Risk Rank Estimation: RBM of Rotating Equipment and Instrumentation
,”
J. Loss Prev. Process Ind.
,
29
, pp.
216
224
.
14.
Hong
,
Y.
,
Pasman
,
H. J.
,
Sachdeva
,
S.
,
Markowski
,
A. S.
, and
Mannan
,
M. S.
,
2016
, “
A Fuzzy Logic and Probabilistic Hybrid Approach to Quantify Uncertainty in Layer of Protection Analysis
,”
J. Loss Prev. Process Ind.
,
43
, pp.
10
17
.
15.
Sankararaman
,
S.
,
Daigle
,
M. J.
, and
Goebel
,
K.
,
2014
, “
Uncertainty Quantification in Remaining Useful Life Prediction Using First-Order Reliability Methods
,”
IEEE Trans. Reliab.
,
63
(
2
), pp.
603
619
.
16.
Sankararaman
,
S.
,
2015
, “
Significance, Interpretation, and Quantification of Uncertainty in Prognostics and Remaining Useful Life Prediction
,”
Mech. Syst. Signal Process.
,
52–53
, pp.
228
247
.
17.
Dubois
,
D.
,
Foulloy
,
L.
,
Mauris
,
G.
, and
Prade
,
H.
,
2004
, “
Probability-Possibility Transformations, Triangular Fuzzy Sets, and Probabilistic Inequalities
,”
Reliab. Comput.
,
10
(
4
), pp.
273
297
.
18.
Pota
,
M.
,
Esposito
,
M.
, and
Pietro
,
G. D.
,
2013
, “
Transforming Probability Distributions Into Membership Functions of Fuzzy Classes: A Hypothesis Test Approach
,”
Fuzzy Sets Syst.
,
233
, pp.
52
73
.
19.
Ross
,
T. J.
,
2010
,
Fuzzy Logic With Engineering Applications
, 3rd ed,
Wiley
,
Chichester, UK
, Chap. 5.
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