Academic Journal
Neural Network-Based Model for Classification of Faults During Operation of a Robotic Manipulator
| العنوان: | Neural Network-Based Model for Classification of Faults During Operation of a Robotic Manipulator |
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| المؤلفون: | Sandi Baressi Šegota, Nikola Anđelić, Zlatan Car, Mario Šercer |
| المصدر: | Tehnički Vjesnik, Vol 28, Iss 4, Pp 1380-1387 (2021) |
| بيانات النشر: | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Engineering (General). Civil engineering (General) |
| مصطلحات موضوعية: | Convolutional Neural Network, Multilayer Perceptron, Robot Fault Detection, Siamese Neural Network, Support Vector Machine, Engineering (General). Civil engineering (General), TA1-2040 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1330-3651 1848-6339 |
| Relation: | https://hrcak.srce.hr/file/379508; https://doaj.org/toc/1330-3651; https://doaj.org/toc/1848-6339 |
| DOI: | 10.17559/TV-20201112163731 |
| URL الوصول: | https://doaj.org/article/95303e2a99584c71939a3c3c92e5edf0 |
| رقم الانضمام: | edsdoj.95303e2a99584c71939a3c3c92e5edf0 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 13303651 18486339 |
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| DOI: | 10.17559/TV-20201112163731 |