API RP 581 - 3rd Ed.2016 - Add.2-2020 - Risk-Based Inspection Methodology
2-32 API RECOMMENDED PRACTICE 581Table 4.4—Data Required for Determination of the Thinning DFBasic DataThinning type (general or localized)Corrosion rate (mmpy or mpy)Inspection effectiveness categoryNumber of inspectionsOn-line monitoringThinning mechanismPresence of injection/mix point(Yes or No)Type of injection/mix point inspectionPresence of a dead-leg (Yes or No)Type of inspection for dead-legcorrosionWelded constructionCommentsDetermine whether the thinning is general or localized based on inspectionresults of effective inspections. General corrosion is defined as affecting morethan 10 % of the surface area and the wall thickness variation is less than1.27 mm (50 mils). Localized corrosion is defined as affecting less than 10 % ofthe surface area or a wall thickness variation greater than 1.27 mm (50 mils).The current rate of thinning calculated from thickness data, if available.Corrosion rates calculated from thickness data typically vary from one inspectionto another. These variations may be due to variations in the wall thickness, orthey may indicate a change in the actual corrosion rate. If the short-term rate(calculated from the difference between the current thickness and the previousthickness) is significantly different from the long-term rate (calculated from thedifference between the current thickness and the original thickness), then thecomponent may be evaluated using the short-term rate, but the appropriate timeand thickness must be used.The effectiveness category of each inspection that has been performed on thecomponent during the time period (specified above).The number of inspections in each effectiveness category that have beenperformed during the time period (specified above).The types of proactive on-line monitoring methods or tools employed, such ascorrosion probes, coupons, process variables (coupons, probes, processvariables, or combinations, etc.).If credit is to be taken for on-line monitoring, the potential thinning mechanismsmust be known. A knowledgeable materials/corrosion engineer should beconsulted for this information; also see API 571 [13] .For piping, determine if there is an injection or mix point in the circuit.For piping circuits that contain an injection or mix point, determine whether notthe inspection program is highly effective or not highly effective to detect localcorrosion at these points.For piping, determine if there is a dead-leg in the circuit.For piping circuits that contain a dead-leg, determine if the inspection programcurrently being used is highly effective or not highly effective to detect localcorrosion in dead-legs has been performed.Applicable to ASTs only, ASTs may be welded or riveted construction. Is theAST of welded construction? (Yes or No)Maintained to API 653 Applicable to ASTs only. Is the AST maintained in accordance with API 653 [11] ?(Yes or No)SettlementApplicable to ASTs only, ASTs may be welded or riveted construction. What isthe settlement history of the AST:— recorded settlement exceeds API 653 criteria;— recorded settlement meets API 653 criteria;— settlement never evaluated;— concrete foundation, no settlement.
RISK-BASED INSPECTION METHODOLOGY, PART 2—PROBABILITY OF FAILURE METHODOLOGY 2-33Table 4.5—Prior Probability for Thinning Corrosion RateDamage State Low Confidence Data Medium Confidence Data High Confidence DataThinPrp10.5 0.7 0.8ThinPrp20.3 0.2 0.15ThinPrp30.2 0.1 0.05Table 4.6—Conditional Probability for Inspection EffectivenessConditional Probabilityof InspectionE—None orIneffectiveD—PoorlyEffectiveC—FairlyEffectiveB—UsuallyEffectiveA—HighlyEffectiveThinCo p10.33 0.4 0.5 0.7 0.9ThinCo p20.33 0.33 0.3 0.2 0.09ThinCo p30.33 0.27 0.2 0.1 0.01
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RISK-BASED INSPECTION METHODOLOGY, PART 2—PROBABILITY OF FAILURE METHODOLOGY 2-33
Table 4.5—Prior Probability for Thinning Corrosion Rate
Damage State Low Confidence Data Medium Confidence Data High Confidence Data
Thin
Prp1
0.5 0.7 0.8
Thin
Prp2
0.3 0.2 0.15
Thin
Prp3
0.2 0.1 0.05
Table 4.6—Conditional Probability for Inspection Effectiveness
Conditional Probability
of Inspection
E—None or
Ineffective
D—Poorly
Effective
C—Fairly
Effective
B—Usually
Effective
A—Highly
Effective
Thin
Co p1
0.33 0.4 0.5 0.7 0.9
Thin
Co p2
0.33 0.33 0.3 0.2 0.09
Thin
Co p3
0.33 0.27 0.2 0.1 0.01
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