عنوان المقالة:Studying the Condition Based Maintenance Dataset of Naval Propulsion Plants Using Regression ANN Studying the Condition Based Maintenance Dataset of Naval Propulsion Plants Using Regression ANN
احمد حسن احمد حسن | Ahmed Hassan Ahmed Hassan | 5752
- نوع النشر
- مؤتمر علمي
- المؤلفون بالعربي
- Mohamad Mehyo; Hakan Özcan; Ahmed Hassan Ahmed Hassan
- المؤلفون بالإنجليزي
- Mohamad Mehyo; Hakan Özcan; Ahmed Hassan Ahmed Hassan
- الملخص العربي
- In this work, applying and experimenting various options effects of a feed-foreword artificial neural network (ANN) which used to obtain regression model that predicts GT Compressor decay state coefficient and GT Turbine decay state coefficient of naval propulsion plants. Based on 16 inputs. Dataset is obtained from an open online source, consists of huge amount of data point, 11935 data points have been carried out by means of a numerical simulator of a naval vessel (Frigate) characterized by a Gas Turbine (GT) propulsion plant. The different blocks forming the complete simulator (Propeller, Hull, GT, Gear Box, and Controller) have been developed and fine-tuned over the year on several similar real propulsion plants. In view of these observations, the available data are in agreement with a possible real vessel. Many neural networks are created and trained with various settings and sub-datasets and then compared to observe the effect of each variation. After the factors which affect the learning operation was studied. It is taken some of the data in order to test the best ANN that had been learned by taking 20 random values from all data and compare their results with the actual outputs.
- الملخص الانجليزي
- In this work, applying and experimenting various options effects of a feed-foreword artificial neural network (ANN) which used to obtain regression model that predicts GT Compressor decay state coefficient and GT Turbine decay state coefficient of naval propulsion plants. Based on 16 inputs. Dataset is obtained from an open online source, consists of huge amount of data point, 11935 data points have been carried out by means of a numerical simulator of a naval vessel (Frigate) characterized by a Gas Turbine (GT) propulsion plant. The different blocks forming the complete simulator (Propeller, Hull, GT, Gear Box, and Controller) have been developed and fine-tuned over the year on several similar real propulsion plants. In view of these observations, the available data are in agreement with a possible real vessel. Many neural networks are created and trained with various settings and sub-datasets and then compared to observe the effect of each variation. After the factors which affect the learning operation was studied. It is taken some of the data in order to test the best ANN that had been learned by taking 20 random values from all data and compare their results with the actual outputs.
- تاريخ النشر
- 03/05/2019
- الناشر
- 3nd International Students Science Congress 3-4 May 2019, Izmir - Turkey
- رقم المجلد
- رقم العدد
- رابط خارجي
- https://www.researchgate.net/publication/335967196_Studying_the_Condition_Based_Maintenance_Dataset_of_Naval_Propulsion_Plants_Using_Regression_ANN
- الكلمات المفتاحية
- Artificial Neural Network, MATLAB Neural Networks Toolbox, Naval Propulsion Plants, Modeling.