首页|Findings in Machine Learning Reported from Gyeongsang National University (A Stu dy on Machine Learning-Based Feature Classification for the Early Diagnosis of B lade Rubbing)
Findings in Machine Learning Reported from Gyeongsang National University (A Stu dy on Machine Learning-Based Feature Classification for the Early Diagnosis of B lade Rubbing)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
A new study on artificial intelligence is now available. According to news reporting originating from Gyeongsang Natio nal University by NewsRx correspondents, research stated, "This research focuses on the development of a machine learning-based approach for the early diagnosis of blade rubbing in rotary machinery." Financial supporters for this research include The Grant Entitled Development of Automatic Predictive Diagnosis Technology; The Gyeongsang National University. The news reporters obtained a quote from the research from Gyeongsang National U niversity: "In this paper, machine learning-based diagnostic methods are used fo r blade rubbing early diagnosis, and the faults are simulated using experimental models. The experimental conditions were simulated as follows: Excessive rotor vibration is generated by an unbalance mass, and blade rubbing occurs through ex cessive rotor vibration. Additionally, the severity of blade rubbing was also si mulated while increasing the unbalance mass. And then, machine learning-based di agnostic methods were applied and the trends according to the severity of blade rubbing were compared. This paper provides a signal processing method through fe ature analysis to diagnose blade rubbing conditions in machine learning."
Gyeongsang National UniversityCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
