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D. C. Sin(신동춘),Tan Andy(앤디 탄),H. E. Jeong(정한얼),B. K. Choi(최병근),W. C. Kim(김원철) 한국동력기계공학회 2007 한국동력기계공학회 학술대회 논문집 Vol.- No.-
The rotary blood pumps with a magnetically suspended impeller has shown its superiority as compared to other blood pumps. However, there is still insufficient understanding of fluid dynamics related issues in the clearance gap of a BVAD. Hence, our research focus is in the prediction of blood trauma in the clearance gap of a BVAD. Computational Fluid Dynamics (CFD) is useful for estimating blood damage through the disk clearance of a magnetically suspended centrifugal blood pump for design of a BV AD centrifugal heart pump. In this study, CFD analysis was performed to quantify scalar shear stress through the leakage path and investigated the effects of axial clearance and rotational speed on scalar shear stress to evaluate prototype pump design.
Hack-Eun Kim,Sung-Soo Hwang,Andy C. C. Tan,Joseph Mathew,최병근 대한기계학회 2012 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.26 No.11
Effective machine fault prognostic technologies can lead to elimination of unscheduled downtime and increase machine useful life and consequently lead to reduction of maintenance costs as well as prevention of human casualties in real engineering asset management. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique and historical failure knowledge embedded in the closed loop diagnostic and prognostic system. To estimate a discrete machine degradation state which can represent the complex nature of machine degradation effectively, the proposed prognostic model employed a classification algorithm which can use a number of damage sensitive features compared to conventional time series analysis techniques for accurate long-term prediction. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for the comparison of intelligent diagnostic test using five different classification algorithms. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state probability using the Support Vector Machine (SVM) classifier. The results obtained were very encouraging and showed that the proposed prognostics system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.
Myeongsu Kang,Jaeyoung Kim,Jong-Myon Kim,Tan, Andy C. C.,Kim, Eric Y.,Byeong-Keun Choi Institute of Electrical and Electronics Engineers 2015 IEEE transactions on power electronics Vol. No.
<P>This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposed GA-based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.</P>
Widodo, Achmad,Yang, Bo-Suk,Kim, Eric Y.,Tan, Andy C. C.,Mathew, Joseph Taylor Francis 2009 Nondestructive testing and evaluation Vol.24 No.4
<P> This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using an AE sensor with the test bearing operating at a constant loading (5 kN) and with a speed range from 20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.</P>