• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1314-1323
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• 2018

For nuclear power plants (NPPs) to have long lifetimes, ageing is a major issue. Currently, ageing management for NPP systems is based on correlations built from generic experimental data. However, each system has its own characteristics, operational history, and environment. To account for this, it is possible to resort to prognostics that predicts the future state and time to failure (TTF) of the target system by updating the generic correlation with specific information of the target system. In this paper, we present an application of particle filtering for the prediction of degradation in steam generator tubes. With a case study, we also show how the prediction results vary depending on the uncertainty of the measurement data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.939-949
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• 2017

Prognostics and health management(PHM) is actively utilized by industry as an essential technology focusing on accurately monitoring the health state of a system and predicting the remaining useful life(RUL). An effective PHM is expected to reduce maintenance costs as well as improve safety of system by preventing failure in advance. With these advantages, PHM can be applied to the battery system which is a core element to provide electricity for devices with mobility, since battery faults could lead to operational downtime, performance degradation, and even catastrophic loss of human life by unexpected explosion due to non-linear characteristics of battery. In this paper we mainly review a recent progress on various models for predicting RUL of battery with high accuracy satisfying the given confidence interval level. Moreover, performance evaluation metrics for battery prognostics are presented in detail to show the strength of these metrics compared to the traditional ones used in the existing forecasting applications.

• Nuclear Engineering and Technology
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• v.50 no.1
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• pp.88-96
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• 2018

Concerns over reliability assessments of the main components in nuclear power plants (NPPs) related to aging and continuous operation have increased. The conventional reliability assessment for main components uses experimental correlations under general conditions. Most NPPs have been operating in Korea for a long time, and it is predictable that NPPs operating for the same number of years would show varying extent of aging and degradation. The conventional reliability assessment does not adequately reflect the characteristics of an individual plant. Therefore, the reliability of individual components and an individual plant was estimated according to operating data and conditions. It is essential to reflect aging as a characteristic of individual NPPs, and this is performed through prognostics. To handle this difficulty, in this paper, the general path model/Bayes, a data-based prognostic method, was used to update the reliability estimated from the generic database. As a case study, the authors consider the aging for steam generator tubes in NPPs and demonstrate the suggested methodology with data obtained from the probabilistic algorithm for the steam generator tube assessment program.

• Journal of IKEEE
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• v.25 no.4
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• pp.689-696
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• 2021

In this paper, in order to apply Prognostics and Health Management(PHM) to an electronic system or circuit, a circuit capable of detecting and predicting defect characteristics inside the system or circuit is implemented, and the results are described. In the previous study, we demonstrated that the frequency of the amplitude of S-parameter changed as the circuit defect progressed. These characteristics were measured by network analyser. but in this study, even if the same defect detection method is used, a circuit is proposed to check the progress of the defect, the remaining time, and the occurrence of the defect without large measurement devices. The circuit is designed to detect the change in impedance that generates changes of S-parameter, and it is verified through simulation using the measurement results of Bond-wires.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.38-48
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• 2004

Recently the use of high-speed equipment in machine-tool industry has greatly increased, which requires the development of prognostics and prediction methods on the design stage. Conversion of the test/experiments stage from real to virtual reality will not only significantly reduce the design and manufacturing cost, but will also increase design quality. This paper shows how it is possible to develop the automated system for the design calculations of the air-bearing spindles. First, the general calculation method is introduced. It contains several steps, namely, geometry identification, pressure calculation, stiffiness calculation, dynamics characteristics calculation. For geometry identification reducing spindle shaft to rings was proposed, which helps to automate the calculation process. For pressure calculation the Peshti method was implemented. For stiffiness calculation the analysis was made, which shown the necessity of correct calculation step selection. Then the system of ordinary differential equations containing influence coefficients was evolved, which is used for trjectories calculation. The graphical representation of the calculation results shows the dynamic behavior of the spindle unit concerning various working conditions. Finally, this automated system is illustrated by an example of the air-bearing spindle calculation.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1995.10a
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• pp.75-79
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• 1995

전기재해(전기재해)라 함은 전력 계통의 이상이나 또는 전기 설비의 누전과 합선 및 과부하나 스파크 등에 의해서 전기 화재를 유발시키거나 전기기기에 이상을 주는 것을 말한다. 그러나 전기재해의 징후를 검출할 수 있다면 최근 들어 계속 늘어만 가는 전기재해의 피해를 줄일 수 있다. 본 논문에서는 전기재해 징후로서의 스파크 현상 검출과 이와 유사한 현상을 보이는 정상 부하의 동작 현상을 비교하고 구분하는 법을 제시하고 있다. 이를 바탕으로 스파크를 수반하는 전기화재의 조기 검출 알고리즘을 소개하였다.

• Smart Structures and Systems
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• v.31 no.3
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• pp.275-281
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• 2023

In recent years, technological developments have rapidly increased the number of complex structures and equipment in the industrial. Accordingly, the prognostics and health monitoring (PHM) technology has become significant. The safety assessment of industrial sites requires data obtained by installing a number of sensors in the structure. Therefore, digital twin technology, which forms the core of the Fourth Industrial Revolution, is attracting attention in the safety field. The research on digital twin technology of structures subjected to seismic loads has been conducted recently. Hence, this study proposes a digital twin system that estimates the responses and arbitrary load in real time by utilizing the minimum sensor to a pipe that receives a seismic and arbitrary load. To construct the digital twin system, a finite-element model was created considering the dynamic characteristics of the pipe system, and then updating the finite-element model. In addition, the calculation speed was improved using a finite-element model that applied the reduced-order modeling (ROM) technology to achieve real-time performance. The constructed digital twin system successfully and rapidly estimated the load and the point where the sensor was not attached. The accuracy of the constructed digital twin system was verified by comparing the response of the digital twin model with that derived by using the load estimated from the digital twin model as input in the finite-element model.

• Fire Science and Engineering
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• v.23 no.1
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• pp.7-14
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• 2009

Several electrical loads such as inrush current, normal operation arcing and non-sinusoidal loads have normal current waveforms similar to arc waveforms. To detect arcs in such loads, therefore, it is necessary to analyze difference between current waveforms with or without arcs. In this paper, using apparatuses of arc generation in UL 1699, arcs are generated in these loads and, then, arc current waveforms are investigated in both the time and the frequency domains to find arc characteristics. This investigation shows that arc current signals have shoulders at some zero current points in the time domain and increment of spectrum magnitude in all over frequency domain. It also shows that the arc characteristics at normal operation arcing and non-sinusoidal loads are detected more easily in the frequency domain than in the time domain. This investigated arc characteristics are expected to be utilized as the basis of development of arc-fault circuit interrupters.

• Journal of Applied Reliability
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• v.18 no.2
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• pp.114-121
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• 2018

Purpose: This study proposes a process for evaluating the preventive maintenance policy for a system with degradation characteristics and for calculating the appropriate preventive maintenance cycle using time- and condition-based maintenance. Methods: First, the collected data is divided into the maintenance history lifetime and degradation lifetime, and analysis datasets are extracted through preprocessing. Particle filter algorithm is used to estimate the degradation lifetime from analysis datasets and prior information is obtained using LSE. The suitability and cost of the existing preventive maintenance policy are each evaluated based on the degradation lifetime and by using a minimum repair block replacement model of time-based maintenance. Results: The process is applied to the degradation of the reverse osmosis (RO) membrane in a seawater reverse osmosis (SWRO) plant to evaluate the existing preventive maintenance policy. Conclusion: This method can be used for facilities or systems that undergo degradation, which can be evaluated in terms of cost and time. The method is expected to be used in decision-making for devising the optimal preventive maintenance policy.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.445-454
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• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.