Neural Network-Based Model for Classification of Faults During Operation of a Robotic Manipulator (CROSBI ID 296982)
Prilog u časopisu | prethodno priopćenje | međunarodna recenzija
Podaci o odgovornosti
Baressi Šegota, Sandi ; Anđelić, Nikola ; Car, Zlatan ; Šercer, Mario
engleski
Neural Network-Based Model for Classification of Faults During Operation of a Robotic Manipulator
The importance of error detection is high, especially in modern manufacturing processes where assembly lines operate without direct supervision. Stopping the faulty operation in time can prevent damage to the assembly line. Public dataset is used, containing 15 classes, 2 types of faultless operation and 13 types of faults, with 463 force and torsion datapoints. Four different methods are used: Multilayer Perceptron (MLP) selected due to high classification performance, Support Vector Machines (SVM) commonly used for a low number of datapoints, Convolutional Neural Network (CNN) known for high performance in classification with matrix inputs and Siamese Neural Network (SNN) novel method with high performance in small datasets. Two classification tasks are performed-error detection and classification. Grid search is used for hyperparameter variation and F1 score as a metric, with a 10 fold cross-validation. Authors propose a hybrid system consisting of SNN for detection and CNN for fault classification.
Convolutional Neural Network ; Multilayer Perceptron ; Robot Fault Detection ; Siamese Neural Network ; Support Vector Machine
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o izdanju
28 (4)
2021.
1380-1387
objavljeno
1330-3651
1848-6339
Trošak objave rada u otvorenom pristupu
Povezanost rada
Elektrotehnika, Računarstvo, Strojarstvo, Temeljne tehničke znanosti