Fault detection in industrial automation systems with artificial intelligence methods
Is this your thesis?
This record came from a bulk archive import. If it’s yours, link it to your profile.
Abstract (EN)
In this thesis; the data sets collected from real production lines are evaluated by using artificial intelligence methods to determine the faults in automation systems.These real datasets collected from production lines are obtained in line with the concepts of "Internet of Things" and "Big Data" which entered our lives with Industry 4.0. These data processed by using Perceptron algorithm (which is an algorithm of Artificial Neural Network), Random Forest algorithm and Gradient Boosting (GBM) algorithms and then the system failures are classified. By analyzing the performances of the artificial intelligence methods, the most appropriate classification method is determined for the selected model. The results of this thesis show that artificial intelligence methods can be used successfully in fault detection for industrial automation systems.
Author
Oğuzhan Çömlekci
How to Cite
Oğuzhan Çömlekci (Master Thesis). Fault detection in industrial automation systems with artificial intelligence methods, 2020, Bursa Uludağ Üni̇versi̇ty.
License
Tüm Hakları Saklıdır
This work is shared under the specified license terms.
More theses from Bursa Uludağ Üni̇versi̇ty
- The effect of subthreshold bipolar disorder symptomatologyon neuropsychological profiles in children and adolescents withattention deficit and hyperactivity disorder(2022)
- Analysis of Ayman al Otoom's "Ya Sâhibay al-Sijn" in terms of structure and content in the context of prison literature(2022)
- The discrete divisions of Hanefi fakihs in the field of criminal law(2020)
- Bayt al-Hikmah and its importance during translation period(2020)
- New security problem in 21th century: Climate refugees(2020)
- Une etude sur les valeurs educatives des livres pour enfants de Daniel Pennac et leurs exploitations en fle(2020)