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Statistical Health Reasoning of Water-Cooled Power Generator Stator Bars Against Moisture Absorption
Youn, Byeng D.,Kyung Min Park,Chao Hu,Joung Taek Yoon,Hee Soo Kim,Beom Chan Jang,Yong Chae Bae IEEE 2015 IEEE transactions on energy conversion Vol.30 No.4
<P>The power generator is typically maintained with a time- or usage-based strategy, which could result in a substantial waste of remaining useful life, high maintenance cost, and low plant availability. Recently, the field of prognostics and health management offers diagnostic and prognostic techniques to precisely assess the health condition and robustly predict the remaining useful life (RUL) of an engineered system, with an aim to address the aforementioned deficiencies. This paper explores a smart health reasoning system to assess the health condition of power generator stator bars against moisture absorption based on the statistical analysis of the capacitance measurements on bar insulators. In particular, a relative health measure, namely the directional Mahalanobis distance, is proposed to quantify the health condition of a stator bar. The smart health reasoning system is validated using five years' field data from seven generators, each of which contains 42 turns.</P>
불인지 불확실성을 고려한 CAE 해석 모델의 예측 정확도 개선
윤병동(Byeng D. Youn),유민지(Minji Yoo),이규석(Guesuk Lee),손혜정(Hyejeong Son),오현석(Hyunseok Oh) 대한기계학회 2015 대한기계학회 춘추학술대회 Vol.2015 No.11
In the design of passenger vehicles in the automotive industry, virtual testing is widely used. However, building a highly-predictive CAE model is still challenging. In this paper, to build a highly-predictive CAE model, several issues in statistical model validation approach are studied with an example of the steering column in cockpit modules: (1) selecting unknown input variables, (2) calibrating unknown parameters, and finally (3) checking the validity of CAE model using area metric based hypothesis testing. It is expected that the study helps to enhance the predictive capability of CAE models.
저널베어링 상태 진단을 위한 최적의 데이터 분석 기준 설정
윤병동(Byeng D. Youn),정준하(Joonha Jung),전병철(Byungchul Jeon),김연환(Yeon-Whan Kim),배용채(Yong-Chae Bae) 한국소음진동공학회 2014 한국소음진동공학회 학술대회논문집 Vol.2014 No.10
Data-driven method for fault diagnostics system often use machine learning technique. To use such technique proper signal processing should be implemented such as time synchronous averaging (TSA) for ball bearing systems. However, for journal bearing diagnostics systems not much has been researched, and yet a proper signal processing method has not been studied. Therefore, in this research an optimal datum unit for a reliable journal bearing diagnostics system along with angular resampling process is being suggested. Before extracting time and frequency domain features, angular resampling is applied to each cycle of vibration data. As to preserve the characteristics of vibration signal, averaging method is replaced by finding the optimal datum unit which strengthens statistical characteristics of vibration signal. Then 20 features were extracted for various cases, and those features are being evaluated by two criteria, separability and classification accuracy.
모듈형 시스템 설계를 위한 플러그인 디지털 해석모델의 통계적 보정 및 검증 기술 최신 동향과 전망
윤병동(Byeng D. Youn),윤헌준(Heonjun Yoon),박정호(Jungho Park),이규석(Guesuk Lee),오현석(Hyunseok Oh) 대한기계학회 2014 대한기계학회 춘추학술대회 Vol.2014 No.11
As engineered products become more complex with a shorter development cycle, many leading conglomerates have introduced the concept of modular system design. With this trend, the role of virtual testing using a computer-aided engineering (CAE) model has increased. However, it is challenging to build a high-fidelity CAE model for plug-in digital analysis of a modular system due to the interactions of modules and joints as well as the complicated nature of the full system. One possible solution is statistical model calibration and validation which can improve and ensure the predictive capability of a CAE model. The purpose of this study is to review the current trends and future directions in model calibration and validation for plug-in digital analysis. Three main areas are focused: (i) correlation metric, (ii) model calibration, and (iii) model validation. This paper attempts to provide insights for adopting an appropriate technique in model calibration and validation for a modular system design.
Fick’s second law 를 이용한 수냉식 발전기 고정자 권선의 건전성 예지
윤병동(Byeng D. Youn),장범찬(Beom-Chan Jang),김희수(Hee-Soo Kim),배용채(Yong-Chae Bae) 대한기계학회 2014 대한기계학회 춘추학술대회 Vol.2014 No.11
Power generator is one of the most important component of electricity generation system to convert mechanical energy to electrical energy. It designed robustly to maintain high system reliability during operation time. But unexpected failure of the power generator could happen and it cause huge amount of economic and social loss. To keep it from unexpected failure, health prognostics should be carried out In this research, We developed a health prognostic method of stator windings in power generator with statistical data analysis and degradation modeling against water absorption. We divided whole 42 windings into two groups, absorption suspected group and normal group. We built a degradation model of absorption suspected winding using Fick’s second law to predict upcoming absorption data. Through the analysis of data of normal group, we could figure out the distribution of data of normal windings. After that, we can properly predict absorption data of normal windings. With data prediction of two groups, we derived upcoming Directional Mahalanobis Distance (DMD) of absorption suspected winding and time vs DMD curve. Finally we drew the probability distribution of Remaining Useful Life of absorption suspected windings.