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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.
터빈 블레이드 진단을 위한 회전기계 마찰 진동에 관한 연구
유현탁(Hyeon Tak Yu),안병현(Byung Hyun Ahn),이종명(Jong Myeong Lee),하정민(Jeong Min Ha),최병근(Byeong Keun Choi) 한국소음진동공학회 2016 한국소음진동공학회 논문집 Vol.26 No.6
Rubbing and misalignment are the most usual faults that occurs in rotating machinery and with them severe effect on power plant availability. Especially blade rubbing is hard to detect on FFT spectrum using the vibration signal. In this paper, the possibility of feature analysis of vibration signal is confirmed under blade rubbing and misalignment condition. And the lab-scale rotor test device provides the blade rubbing and shaft misalignment modes. Feature selection based on GA (genetic algorithm) is processed by the extracted feature of the time domain. Then, classification of the features is analyzed by using SVM (support vector machine) which is one of the machine learning algorithm. The results of features selection based on GA compared with those based on PCA (principal component analysis). According to the results, the possibility of feature analysis is confirmed. Therefore, blade rubbing and shaft misalignment can be diagnosed by feature of vibration signal.
관주형 철탑 상태 감시를 위한 음향 방출 신호처리에 따른 특징 분석
유현탁(Hyeon-Tak Yu),민태홍(Tae-Hong Min),김형진(Hyeong-Jin Kim),강석근(Seoggeun Kang),강동영(Dong-Young Kang),김현식(Hyun-Sik Kim),최병근(Byeong-Keun Choi) 한국소음진동공학회 2021 한국소음진동공학회 논문집 Vol.31 No.2
In this study, we propose and analyze a machine learning method based on the genetic algorithm (GA) and supporting vector machine (SVM) for the effective classification of faults detected by an acoustic emission test on the welding parts of tubular steel towers. A band-pass filter, an envelope analysis (EA), and an intensified EA (IEA) are employed to generate feature vectors for the machine learning method based on the GA. After signal processing, the signals are applied to GA-based machine learning to derive the representative features of the received signal, and the SVM classifies the fault signals and normal signals from the detected signals. Consequently, it is confirmed that the received signal processed by EA and IEA can classify faults with an accuracy of 93 % or more. Hence, the proposed fault test and classification method is expected to be useful in the development of a system for constant monitoring and early detection of welding faults inside a tubular steel tower.
이종명(Jong-Myeong Lee),유현탁(Hyeon-Tak Yu),박규진(Gyu-Jin Park),최현철(Hyeon-Cheol Choi),최병근(Byeong-Keun Choi) 한국소음진동공학회 2015 한국소음진동공학회 논문집 Vol.25 No.7
This paper provides how to solve the problems analytically and experimentally that occur for testing the water injection pump under development. First of all, water injection pump, based on shaft system dynamic analysis, is verified by measuring the behavior of the shaft system. After the water injection pump is measured, the structural resonances which can cause excessive noise, degradation the equipment life and malfunction are found. Therefore, by changing the structural design, the resonance should be avoided. Application of the design variables to the experimentally resonance avoidance is difficult. So analytically, with application of the design variables, the design will be changed with mode analysis using FEM.