Classification of Faults Operation of a Robotic Manipulator Using Symbolic Classifier

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@Article{Andelic:2023:applsci,

• author = "Nikola Andelic and Ivan Lorencin and Sandi {Baressi Segota} and Zlatan Car",
• title = "Classification of Faults Operation of a Robotic Manipulator Using Symbolic Classifier",
• journal = "Applied Sciences",
• year = "2023",
• volume = "13",
• number = "3",
• pages = "Article no 1962",
• month = feb,
• email = "nandelic@riteh.hr",
• keywords = "genetic algorithms, genetic programming, oversampling methods, robot fault operation, random oversampling, symbolic classifier, SMOTE",
• publisher = "MDPI",
• ISSN = "2076-3417",
• URL = "https://www.mdpi.com/2076-3417/13/3/1962",
• DOI = "doi:10.3390/app13031962",
• size = "23 pages",
• abstract = "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...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.",

}

Genetic Programming entries for Nikola Andelic Ivan Lorencin Sandi Baressi Segota Zlatan Car

Citations