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Yoon, Joung Taek,Youn, Byeng D.,Yoo, Minji,Kim, Yunhan,Kim, Sooho Elsevier 2019 Reliability engineering & system safety Vol.184 No.-
<P><B>Abstract</B></P> <P>Fault diagnosis aims to diagnose system failures and to enable timely maintenance that, in turn, can minimize system maintenance costs. In order to evaluate and maximize the benefits from fault diagnosis, the life‐cycle maintenance cost should be analyzed. This paper presents a framework for life‐cycle maintenance cost analysis that considers time‐dependent false and missed alarms in fault diagnosis. First, time‐dependent false and missed alarms are proposed. The false and missed alarm rates are not static but vary depending on the health state of the engineered system, which changes over time. Second, a life‐cycle maintenance cost analysis framework is proposed. This is based upon a stochastic simulation method that can incorporate time‐dependent false and missed alarm rates and various uncertainties, such as health degradation and health restoration through maintenance. Third, a fault diagnosis model design method is proposed, based upon the proposed life‐cycle maintenance cost analysis framework. The proposed method incorporates optimal false and missed alarm weights into the life‐cycle maintenance cost. As a result, the proposed ideas enable accurate estimation and minimization of the overall life‐cycle maintenance cost. The effectiveness of the proposed methods is demonstrated via a numerical example and an electro‐hydrostatic actuator case study.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A life‐cycle maintenance cost analysis method for fault diagnosis is proposed. </LI> <LI> Time‐dependent false and missed alarms in fault diagnosis are considered. </LI> <LI> A false and missed alarm weight optimization method is proposed. </LI> <LI> A life‐cycle maintenance cost is minimized with the proposed methods. </LI> </UL> </P>
Vibration-based Health Index Extraction of Power Transformers
Joung Taek Yoon(윤정택),Kyung Min Park(박경민),Byeng Dong Youn(윤병동),Wook-Ryun Lee(이욱륜) 대한기계학회 2012 대한기계학회 춘추학술대회 Vol.2012 No.11
Power transformer is the one of key components of power plants as well as the most frequently failed components. As the failure of power transformers can cause whole plant shut-down and substantial capital loss, failure prediction techniques, i.e., health diagnostics and prognostics, are vital. This study aims at developing a health diagnostics method for oil-filled power transformers using mechanical vibration signal against two mechanical failure modes. The vibration signals are acquired from on-site 36 transformers and two health metrics-mean and standard deviation of spectral responses-are proposed based upon a sensitivity analysis. From the fact that transformers with greater health metrics have long operation years than those with smaller it can be concluded that the proposed health metrics are suitable for health diagnostics and fault identification of power transformers.
A newly formulated resilience measure that considers false alarms
Yoon, Joung Taek,Youn, Byeng D.,Yoo, Minji,Kim, Yunhan Elsevier 2017 Reliability engineering & system safety Vol.167 No.-
<P><B>Abstract</B></P> <P>Engineering resilience is a measure of a system's ability to maintain its functionality by resisting and recovering from adverse events. Although false alarms often occur in engineering practice, existing quantitative measures of engineering resilience do not consider false alarms. This makes it difficult to estimate the true degree of resilience and impedes efforts to design a resilient engineered system. This paper thus proposes a new resilience measure that considers false alarms. Two types of false alarms, Type I (false fault) and Type II (false health), are considered and quantified based upon conditional probability theory. A new resilience measure, which considers false alarm rates and reliability, is then formulated in a probabilistic manner. Compared to the conventional resilience measure, the newly formulated resilience measure can estimate system resilience more rigorously and accurately. The effectiveness of the proposed resilience measure is demonstrated via numerical and electro‐hydrostatic actuator case studies.</P> <P><B>Highlights</B></P> <P> <UL> <LI> To the best of our knowledge, a resilience measure considering false alarms is proposed for the first time. </LI> <LI> The state-of-the-art resilience measures are reviewed and their limitations are underlined. </LI> <LI> The effect of false alarms on system resilience is explored in a probabilistic way. </LI> <LI> Case studies showed the superiority of the proposed resilience measure to the existing one. </LI> </UL> </P>
전력용 변압기 기계적 고장 진단을 위한 센서 네크워크 최적화 방법론
윤정택(Joung Taek Yoon),윤병동(Byeng D. Youn),박경민(Kyung Min Park),이욱륜(Wook-Ryun Lee) 대한기계학회 2013 대한기계학회 춘추학술대회 Vol.2013 No.12
Power transformer is one of the key components in power plants as well as the most frequently failed components. As the failure of power transformers can cause plant shut-down and substantial capital loss, failure prediction techniques, i.e., fault diagnostics and prognostics, are vital. To prevent the mechanical failures of power transformers, vibrations sensors are installed on the surface of oil-filled power transformers. Due to high randomness in vibration and large scale of the transformers, an excessively large number of sensors are generally installed. This study aims at developing the framework of sensor network (SN) optimization to maximize the diagnostic and prognostic capability of the transformers’ potential faults. The vibrations of 36 on-site power transformers were measured. The analysis of the vibrations shows transformer tank vibration characteristics and the importance of high vibration signals in fault detection. The proposed SN optimization framework optimizes the number of sensors and their locations to measure the high vibration signals robustly against system uncertainty. Comparing evaluated health status with maintenance history demonstrates that the proposed framework is capable of estimating transformer health status with the significantly
Clinical characteristics and follow-up of Korean patients with adrenal incidentalomas
( Yoon Young Cho ),( Sung Hwan Suh ),( Ji Young Joung ),( Hyem In Jeong ),( Dong Mo Je ),( Hong Seok Yoo ),( Taek Kyu Park ),( Yong Ki Min ),( Kwang Won Kim ),( Jae Hyeon Kim ) 대한내과학회 2013 The Korean Journal of Internal Medicine Vol.28 No.5
Background/Aims: We investigated the clinical characteristics and follow-up findings of subjects with adrenal incidentalomas in a single, tertiary-care hospital in South Korea. Methods: The study consisted of a retrospective analysis of 282 adrenal incidentaloma patients who underwent radiographic and endocrinological evaluations at Samsung Medical Center in Seoul, South Korea, between January 2004 and July 2011. Results: Most (86.2%) of the subjects were found to have nonfunctioning tumors. Functioning tumors were seen in 39 patients (13.8%). Among them, 28 (9.9%) had subclinical Cushing syndrome (SCS), six (2.1%) had pheochromocytoma, and five (1.8%) had primary hyperaldosteronism. Malignant adrenal tumors were discovered in three cases: two (0.7%) were primary adrenal cancers, and one (0.4%) was a secondary metastasis from a lung cancer. Significant risk factors for functional tumors were female gender (odds ratio [OR], 3.386; 95% confidence interval [CI], 1.611 to 7.117; p = 0.0013) and a noncontrast attenuation value of > 10 Hounsfield units (OR, 2.806; 95% CI, 1.231 to 6.397; p = 0.0141). During follow-up (mean, 22.5 months) of 72 of the patients, three (4.2%) developed hormonal changes due to functional tumors. One was confirmed as pheochromocytoma by histopathology, and the others were diagnosed with SCS and followed routinely without surgical intervention. No malignant transformation was found in these patients. Conclusions: Based on these findings, initial hormonal and radiographic evaluations for adrenal incidentalomas appear to be more important than follow-up tests because functional or malignant changes are rare.