A Study on the Remaining Life Evaluation of Heat Exchanger Tubes Using Extreme Value Analysis

2021 ◽  
Vol 45 (6) ◽  
pp. 523-529
Author(s):  
Eui Jong Yoo
Keyword(s):  
Heat Exchanger ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Remaining Life ◽  
Value Analysis ◽  
Life Evaluation

Extreme Value Analysis of Heat Exchanger Tube Inspection Data

2006 ◽  
Author(s):  
W. David Wang
Keyword(s):  
Heat Exchanger ◽  
Sample Size ◽  
Heat Exchangers ◽  
Extreme Value ◽  
American Petroleum Institute ◽  
Extreme Value Analysis ◽  
Test Cases ◽  
Petrochemical Plant ◽  
Value Analysis ◽  
Inspection Data

This paper presents a statistical extreme value analysis (EVA) method for assessment of the minimum remaining thickness of tubes in industrial heat exchangers, including air-cooled heat exchangers and shell-and-tube heat exchangers. Five test cases are used to evaluate the effectiveness of the EVA method developed in this work. In each case, real inspection data from heat exchangers in petroleum refinery and petrochemical plant are analyzed, and the results discussed. The effectiveness of the EVA method is then evaluated per the Inspection Effectiveness definition used by American Petroleum Institute (API) for Risk-Based Inspection (RBI), so that the EVA method and results can be easily incorporated in API RBI assessment. It is found through these test cases that the most ideal sample size seems to be 20–30 tubes, regardless of the number of tubes in the heat exchanger. At that sample size, A, B, C and D levels of API RBI inspection effectiveness can be achieved by using the lower bound value of the 99%, 95%, 90%, and 80% confidence intervals, respectively.

Modeling European hot spells using extreme value analysis

2014 ◽  
Vol 58 (3) ◽  
pp. 193-207 ◽  
Author(s):  
C Photiadou ◽  
MR Jones ◽  
D Keellings ◽  
CF Dewes
Keyword(s):  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Hot Spells

scholarly journals Threshold selection in univariate extreme value analysis

Extremes ◽  
2021 ◽  
Author(s):  
Laura Fee Schneider ◽  
Andrea Krajina ◽  
Tatyana Krivobokova
Keyword(s):  
Mean Squared Error ◽  
Extreme Value ◽  
Hill Estimator ◽  
Small Samples ◽  
Extreme Value Analysis ◽  
Threshold Selection ◽  
Value Analysis ◽  
Wide Range ◽  
Asymptotic Mean Squared Error ◽  
The Hill

AbstractThreshold selection plays a key role in various aspects of statistical inference of rare events. In this work, two new threshold selection methods are introduced. The first approach measures the fit of the exponential approximation above a threshold and achieves good performance in small samples. The second method smoothly estimates the asymptotic mean squared error of the Hill estimator and performs consistently well over a wide range of processes. Both methods are analyzed theoretically, compared to existing procedures in an extensive simulation study and applied to a dataset of financial losses, where the underlying extreme value index is assumed to vary over time.

Spatial stochastic simulation to aid local extreme value analysis of cyclone-induced wave heights when numerical hydrodynamic simulations are scarce

2021 ◽  
Author(s):  
Jeremy Rohmer ◽  
Rodrigo Pedreros ◽  
Yann Krien
Keyword(s):  
Confidence Interval ◽  
Stochastic Simulation ◽  
Extreme Value ◽  
Return Level ◽  
Extreme Value Analysis ◽  
Percentage Error ◽  
Value Analysis ◽  
Return Levels ◽  
Wave Heights ◽  
Local Extreme

<p>To estimate return levels of wave heights (Hs) induced by tropical cyclones at the coast, a commonly-used approach is to (1) randomly generate a large number of synthetic cyclone events (typically >1,000); (2) numerically simulate the corresponding Hs over the whole domain of interest; (3) extract the Hs values at the desired location at the coast and (4) perform the local extreme value analysis (EVA) to derive the corresponding return level. Step 2 is however very constraining because it often involves a numerical hydrodynamic simulator that can be prohibitive to run: this might limit the number of results to perform the local EVA (typically to several hundreds). In this communication, we propose a spatial stochastic simulation procedure to increase the database size of numerical results with synthetic maps of Hs that are stochastically generated. To do so, we propose to rely on a data-driven dimensionality-reduction method, either unsupervised (Principal Component Analysis) or supervised (Partial Least Squares Regression), that is trained with a limited number of pre-existing numerically simulated Hs maps. The procedure is applied to the Guadeloupe island and results are compared to the commonly-used approach applied to a large database of Hs values computed for nearly 2,000 synthetic cyclones (representative of 3,200 years – Krien et al., NHESS, 2015). When using only a hundred of cyclones, we show that the estimates of the 100-year return levels can be achieved with a mean absolute percentage error (derived from a bootstrap-based procedure) ranging between 5 and 15% around the coasts while keeping the width of the 95% confidence interval of the same order of magnitude than the one using the full database. Without synthetic Hs maps augmentation, the error and confidence interval width are both increased by nearly 100%. A careful attention is paid to the tuning of the approach by testing the sensitivity to the spatial domain size, the information loss due to data compression, and the number of cyclones. This study has been carried within the Carib-Coast INTERREG project (https://www.interreg-caraibes.fr/carib-coast).</p>

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