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RK4를 이용한 삼점지지 Shaft Balancing 실험
유현탁(Hyeon-Tak Yu),이종명(Jong-Myeong Lee),김용석(Yong-Seok Kim),김학은(Hack-Eun Kim),최병근(Byeong-Keun Choi) 한국소음진동공학회 2015 한국소음진동공학회 학술대회논문집 Vol.2015 No.10
The subject of this paper is a high-pressure LNG secondary pump. This pump has a long shaft and integral type of rotor. Also, the shaft has three support points. This shape has difficulty correcting the unbalance using the existing two-plane balancing. we simulated the shaft having three supports by using the Rotor kit (RK4). By carrying out balancing at each rpm, we compared the amplitude, drew the graph and compared every aspect resulted from balancing at each rpm. Then, we checked which rpm the most advantageous balancing occurs at.
안병현(Byung Hyun Ahn),유현탁(Hyeon Tak Yu),최병근(Byeong Keun Choi) Korean Society for Precision Engineering 2018 한국정밀공학회지 Vol.35 No.2
Fault diagnosis and condition monitoring of rotating machines are important for the maintenance of the gas turbine system. In this paper, the Lab-scale rotor test device is simulated by a gas turbine, and faults are simulated such as Rubbing, Misalignment and Unbalance, which occurred from a gas turbine critical fault mode. In addition, blade rubbing is one of the gas turbine main faults, as well as a hard to detect fault early using FFT analysis and orbit plot. However, through a feature based analysis, the fault classification is evaluated according to several critical faults. Therefore, the possibility of a feature analysis of the vibration signal is confirmed for rotating machinery. The fault simulator for an acquired vibration signal is a rotor-kit based test rig with a simulated blade rubbing fault mode test device. Feature selection based on GA (Genetic Algorithms) one of the feature selection algorithm is selected. Then, through the Support Vector Machine, one of machine learning, feature classification is evaluated. The results of the performance of the GA compared with the PCA (Principle Component Analysis) for reducing dimension are presented. Therefore, through data learning, several main faults of the gas turbine are evaluated by fault classification using the SVM (Support Vector Machine).