Pregled bibliografske jedinice broj: 1252150
Classification of Faults Operation of a Robotic Manipulator using Symbolic Classifier
Classification of Faults Operation of a Robotic Manipulator using Symbolic Classifier // Applied sciences (Basel), 13 (2023), 3; 1-23 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1252150 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Classification of Faults Operation of a Robotic
Manipulator using Symbolic Classifier
Autori
Anđelić, Nikola ; Baressi Šegota, Sandi ; Glučina, Matko ; Lorencin, Ivan
Izvornik
Applied sciences (Basel) (2076-3417) 13
(2023), 3;
1-23
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
genetic programming ; oversampling methods ; robot fault operation ; random oversampling ; symbolic classifier ; SMOTE
Sažetak
In autonomous manufacturing lines, it is very important to detect the faulty operation of robot manipulators to prevent potential damage. In this paper, the application of a genetic programming algorithm (symbolic classifier) with a random selection of hyperparameter values and trained using a 5-fold cross- validation process is proposed to determine expressions for fault detection during robotic manipulator operation, using a dataset that was made publicly available by the original researchers. The original dataset was reduced to a binary dataset (fault vs. normal operation) ; however, due to the class imbalance random oversampling, and SMOTE methods were applied. The quality of best symbolic expressions (SEs) was based on the highest mean values of accuracy (ACC), area under receiving operating characteristics curve (AUC), Precision, Recall, and F1−Score. The best results were obtained on the SMOTE dataset with ACC, AUC, Precision, Recall, and F1−Score equal to 0.99, 0.99, 0.992, 0.9893, and 0.99, respectively. Finally, the best set of mathematical equations obtained using the GPSC algorithm was evaluated on the initial dataset where the mean values of ACC, AUC, Precision, Recall, and F1−Score are equal to 0.9978, 0.998, 1.0, 0.997, and 0.998, respectively. The investigation showed that using the described procedure, symbolically expressed models of a high classification performance are obtained for the purpose of detecting faults in the operation of robotic manipulators.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Strojarstvo, Interdisciplinarne tehničke znanosti
POVEZANOST RADA
Projekti:
--uniri-mladi-technic-22-61 - Energetska optimizacija industrijskih robotskih manipulatora primjenom algoritama evolucijskog računarstva (Anđelić, Nikola) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( CroRIS)
--KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša) ( CroRIS)
--uniri-mladi-technic-22-57 - Razvoj inteligentnog sustava za estimaciju točke maksimalne snage fotonaponskog sustava s primjenom na autonomna plovila (Lorencin, Ivan) ( CroRIS)
undefined
Profili:
Sandi Baressi Šegota (autor)
Nikola Anđelić (autor)
Ivan Lorencin (autor)
Matko Glučina (autor)
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- Social Science Citation Index (SSCI)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus