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김연환,김동환,박선휘,Kim, Yeonwhan,Kim, Donghwan,Park, SunHwi 한국전력공사 2018 KEPCO Journal on electric power and energy Vol.4 No.2
본 논문에서는 가스 터빈 축 진동 신호 비정상 상태 분석의 사례 연구를 위해 커널 회귀 모델을 적용한다. 원격으로 전송되는 발전소 가스터빈의 진동데이터에 커널 회귀 모델을 적용하여 설비를 실시간으로 감시 및 분석 외에도, 축진동 신호의 비정상 상태를 분석하기 위하여 활용될 수 있다. 정상운전 중에 측정한 가스터빈의 정상적인 축진동 데이터 기반의 훈련데이터를 사용하여 생성한 자동연관커널회귀의 경험적 모델을 생성하고 적용할 수 있다. 이 데이터 기반 모델의 예측치를 실시간 데이터와 비교하여 신호의 상태를 분석하고 잔차를 감시하여 이상상태에 대한 분석 정보를 제공할 수 있다. 이상상태에서 발생하는 잔차는 비정상적으로 변화됨으로서 비정상 상태를 분석 할 수 있다. 본 논문에서 커널회귀모델은 축진동 센서의 신호 이상의 원인 분석 사례에서 고장을 구분할 수 있는 정보를 제공한다. In this paper, the kernel regression model is applied for the case study of gas turbine abnormal state analysis. In addition to vibration analysis at the remote site, the kernel regression model technique can is useful for analyzing abnormal state of rotor vibration signals of gas turbine in power plant. In monitoring based on data-driven techniques correlated measurements, the fault free training data of shaft vibration obtained during normal operations of gas turbine are used to develop a empirical model based on auto-associative kernel regression. This data-driven model can be used to predict virtual measurements, which are compared with real-time data, generating residuals. Any faults in the system may cause statistically abnormal changes in these residuals and could be detected. As the result, the kernel regression model provides information that can distinguish anomalies such as sensor failure in a shaft vibration signal.
김희수(Heesoo Kim),배용채(Yongchae Bae),김연환(Yeonwhan Kim),이현(Hyun Lee),김성휘(Sunghwi Kim) 대한기계학회 2003 대한기계학회 춘추학술대회 Vol.2003 No.4
The steady stress, modal analysis for the damaged blade was carried out to evaluate the integrity of LP 4<br/> blade row. As a result, 4 dangerous modes for LP blade row were found in the interference diagram and it<br/> was confirmed that the nozzle passing frequency has nothing to do with the blade failure. And then the<br/> dynamic stress are analysed for the 4 dangerous modes. There are some points far out of maximum allowable<br/> stress in the cover and tenon. Therefore the blade is not safe according to the Goodman judgement. So the<br/> manufacturer have modified the design of cover and tenon. Until now, the power plant is being operated<br/> without special problems.
진동신호 특성 예측 및 분류를 통한 회전체 고장진단 방법
김동환(Donghwan Kim),손석만(Seokman Sohn),김연환(Yeonwhan Kim),배용채(Yongchae Bae) 한국소음진동공학회 2014 한국소음진동공학회 학술대회논문집 Vol.2014 No.10
In this paper, we have developed a new fault detection method based on vibration signal for rotor machinery. Generally, many methods related to detection of rotor fault exist and more advanced methods are continuously developing past several years. However, there are some problems with existing methods. Oftentimes, the accuracy of fault detection is affected by vibration signal change due to change of operating environment since the diagnostic model for rotor machinery is built by the data obtained from the system. To settle a this problems, we build a rotor diagnostic model by using feature residual based on vibration signal. To prove the algorithm’s performance, a comparison between proposed method and the most used method on the rotor machinery was conducted. The experimental results demonstrate that the new approach can enhance and keeps the accuracy of fault detection exactly although the algorithm was applied to various systems.