DPGWO Based Feature Selection Machine Learning Model for Prediction of Crack Dimensions in Steam Generator Tubes
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F23%3A10252698" target="_blank" >RIV/61989100:27230/23:10252698 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001034936600001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001034936600001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/app13148206" target="_blank" >10.3390/app13148206</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
DPGWO Based Feature Selection Machine Learning Model for Prediction of Crack Dimensions in Steam Generator Tubes
Popis výsledku v původním jazyce
The selection of an appropriate number of features and their combinations will play a major role in improving the learning accuracy, computation cost, and understanding of machine learning models. In this present work, 22 gray-level co-occurrence matrix features extracted from magnetic flux leakage images captured in steam generator tubes' cracks are considered for developing a machine learning model to predict and analyze crack dimensions in terms of their length, depth, and width. The performance of the models is examined by considering R-2 and RMSE values calculated using both training and testing data sets. The F Score and Mutual Information Score methods have been applied to prioritize the features. To analyze the effect of different machine learning models, their number of features, and their selection methods, a Taguchi experimental design has been implemented and an analysis of variance test has been conducted. The dynamic population gray wolf algorithm (DPGWO) has been adopted to select the best features and their combinations. Due to the two contradictory natures of performance metrics, Pareto optimal solutions are considered, and the best one is obtained using Deng's method. The effectiveness of DPGWO is proved by comparing its performance with Grey Wolf Optimization and Moth Flame Optimization algorithms using the Friedman test and performance indicators, namely inverted generational distance and spacing.
Název v anglickém jazyce
DPGWO Based Feature Selection Machine Learning Model for Prediction of Crack Dimensions in Steam Generator Tubes
Popis výsledku anglicky
The selection of an appropriate number of features and their combinations will play a major role in improving the learning accuracy, computation cost, and understanding of machine learning models. In this present work, 22 gray-level co-occurrence matrix features extracted from magnetic flux leakage images captured in steam generator tubes' cracks are considered for developing a machine learning model to predict and analyze crack dimensions in terms of their length, depth, and width. The performance of the models is examined by considering R-2 and RMSE values calculated using both training and testing data sets. The F Score and Mutual Information Score methods have been applied to prioritize the features. To analyze the effect of different machine learning models, their number of features, and their selection methods, a Taguchi experimental design has been implemented and an analysis of variance test has been conducted. The dynamic population gray wolf algorithm (DPGWO) has been adopted to select the best features and their combinations. Due to the two contradictory natures of performance metrics, Pareto optimal solutions are considered, and the best one is obtained using Deng's method. The effectiveness of DPGWO is proved by comparing its performance with Grey Wolf Optimization and Moth Flame Optimization algorithms using the Friedman test and performance indicators, namely inverted generational distance and spacing.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
20300 - Mechanical engineering
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Applied Sciences
ISSN
2076-3417
e-ISSN
—
Svazek periodika
13
Číslo periodika v rámci svazku
14
Stát vydavatele periodika
CH - Švýcarská konfederace
Počet stran výsledku
33
Strana od-do
—
Kód UT WoS článku
001034936600001
EID výsledku v databázi Scopus
—