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인공신경망을 이용한 머신러닝 기반의연료펌프 고장예지 연구Study of Fuel Pump Failure Prognostic Based on Machine Learning Using Artificial Neural Network
- Other Titles
- Study of Fuel Pump Failure Prognostic Based on Machine Learning Using Artificial Neural Network
- Issue Date
- 2019
- Publisher
- 한국기계가공학회
- Keywords
- Failure Prognostic(고장예지); Machine Learning(머신러닝); Fuel Pump(연료펌프); Artificial Neural Network(인공신경망); Sensor(센서)
- Citation
- 한국기계가공학회지, v.18, no.9, pp.52 - 57
- Journal Title
- 한국기계가공학회지
- Volume
- 18
- Number
- 9
- Start Page
- 52
- End Page
- 57
- ISSN
- 1598-6721
- Abstract
- The key technology of the fourth industrial revolution is artificial intelligence and machine learning. In this study, FMEA was performed on fuel pumps used as key items in most systems to identify major failure components, and artificial neural networks were built using big data. The main failure mode of the fuel pump identified by the test was coil damage due to overheating. Based on the artificial neural network built, machine learning was conducted to predict the failure and the mean error rate was 4.9% when the number of hidden nodes in the artificial neural network was three and the temperature increased to 140 °C rapidly.
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Collections - School of Mechanical System Engineering > 1. Journal Articles
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Related Researcher
- Hur, Jang Wook
- College of Engineering (School of Mechanical System Engineering)