Operations, Applications & Components

Evaluation of Maintenance Intervals for Spring-Operated Relief Valves Using a Risk-Based Inspection Technique

[+] Author and Article Information
Robert E. Gross

Savannah River Nuclear Solutions,
Aiken, SC 29808

Emily M. Mitchell

Emory University,
Atlanta, GA 30322

Stephen P. Harris

Savannah River National Laboratory,
Aiken, SC 29808

The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States government purposes.

Contributed by the Pressure Vessel and Piping Division of ASME for publication in the JOURNAL OF PRESSURE VESSEL TECHNOLOGY. Manuscript received May 25, 2011; final manuscript received January 9, 2012; published online November 6, 2012. Assoc. Editor: Allen C. Smith.

J. Pressure Vessel Technol 134(6), 061601 (Nov 06, 2012) (7 pages) doi:10.1115/1.4005940 History: Received May 25, 2011; Revised January 09, 2012

The Savannah River Site (SRS) spring-operated relief valve (SORV) maintenance intervals were evaluated using the American Petroleum Institute’s (API) inspection updating approach in API RP 581 for Risk-Based Inspection (RBI) technology. In addition, the impact of extending the inspection schedule was evaluated using Monte Carlo simulation (MCS). The API RP 581 approach is characterized as a Weibull analysis with modified Bayesian updating. Initial Weibull parameter estimates were updated using SRS’s historical proof test records contained in the Center for Chemical Process Safety Process Equipment Reliability Database. The API RP 581 methodology was used to estimate the SORV’s probability of failing on demand and the annual expected risk. An illustration of the inspection costs versus the associated risks is provided. Current practices are reviewed, and recommendations are made for extending maintenance intervals. A cost-effective maintenance frequency balancing both financial risk and inspection cost is demonstrated.

Copyright © 2012 by ASME
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Fig. 1

Contour plot of MTBF (yr) assuming a two-parameter Weibull distribution

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Fig. 2

Hazard plot for η=1.5 yr when β = 0.8, 1, and 1.5

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Fig. 3

The impact of a single pass or fail proof test result on the current estimate of eta (η=20) for various weight factors: λ=0.1, 0.2, 0.3, and 0.4(β=1.5). Note that: (1) for passed inspection at 5 yr, η=20 yr (essentially no change); (2) for failed inspection at 5 yr, η=13 yr.

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Fig. 9

Snapshot of a 56 K Monte Carlo series of η updating for simulated time to failure (β=2.3 and η=21 yr) using (a) the current valve maintenance plan. (b) the current valve maintenance plan extended by 1 yr. and (c) the current valve maintenance plan extended by 2 yr.

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Fig. 8

Updating η based on SRS used valve proof tests

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Fig. 7

Contour plot of the likelihood function, L, using SRS proof test results

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Fig. 6

Box and whisker plots for time-in-service of failed and passed SRS used valve proof tests

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Fig. 5

The cumulative maintenance time distribution for time-in-service for used valves

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Fig. 4

Rp versus time-in-service (years) for the SRS used valve proof tests

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Fig. 10

Risk, inspection cost, and total cost of inspection for extending maintenance intervals



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