• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.814-826
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• 2023

Sensor faults in nuclear power plant instrumentation have the potential to spread negative effects from wrong signals that can cause an accident misdiagnosis by plant operators. To detect sensor faults and make accurate accident diagnoses, prior studies have developed a supervised learning-based sensor fault detection model and an accident diagnosis model with faulty sensor isolation. Even though the developed neural network models demonstrated satisfactory performance, their diagnosis performance should be reevaluated considering real-time connection. When operating in real-time, the diagnosis model is expected to indiscriminately accept fault data before receiving delayed fault information transferred from the previous fault detection model. The uncertainty of neural networks can also have a significant impact following the sensor fault features. In the present work, a pilot study was conducted to connect two models and observe actual outcomes from a real-time application with an integrated system. While the initial results showed an overall successful diagnosis, some issues were observed. To recover the diagnosis performance degradations, additive logics were applied to minimize the diagnosis failures that were not observed in the previous validations of the separate models. The results of a case study were then analyzed in terms of the real-time diagnosis outputs that plant operators would actually face in an emergency situation.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2612-2616
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• 2003

Method for performance monitoring and diagnosis of a MIMO control system has been studied aiming at application to model predictive control (MPC) for industrial processes. The performance monitoring part is designed on the basis of the traditional SPC/SQC method. To meet the underlying premise of Schwart chart observation that the observed variable should be univariate and independent, the process variables are decorrelated temporally as well as spatially before monitoring. The diagnosis part was designed to identify the root of performance degradation among the controller, process, and disturbance. For this, a method to estimate the model-error and disturbance signal has been devised. The proposed methods were evaluated through numerical examples.

• Journal of KIBIM
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• v.11 no.3
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• pp.34-44
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• 2021

This study quantitatively analyzes the work performance of the structural safety diagnosis team that diagnoses pipe racks. To this end, a method for evaluating the performance of the structural safety diagnosis team using the queuing model was proposed. For verification, the case of applying the existing method and the method of introducing a 3D laser scanner for one site was used. The period, number of people, and initial investment cost of each project were collected through interviews with case project experts. As a result of analyzing the performance of the structural safety diagnosis team using the queuing model, it was possible to confirm the probability of delay in the work of each project and the amount of delayed work. Through this, the cost (standby cost) when the project was delayed was analyzed. Finally, economic analysis was conducted in consideration of the waiting cost, labor cost, and initial investment cost. The results of this study can be used to decide whether to introduce 3D laser scanners.

• Journal of Mechanical Science and Technology
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• v.19 no.8
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• pp.1670-1681
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• 2005

In-situ diagnosis of chiller performance is an essential step for energy saving business. The main purpose of the in-situ diagnosis is to predict the performance of a target chiller. Many models based on thermodynamics have been proposed for the purpose. However, they have to be modified from chiller to chiller and require profound knowledge of thermodynamics and heat transfer. This study focuses on developing an easy-to-use diagnostic technique that is based on adaptive neuro-fuzzy inference system (ANFIS). The effect of sample data distribution on training the ANFIS is investigated. It is found that the data sampling over 10 days during summer results in a reliable ANFIS whose performance prediction error is within measurement errors. The reliable ANFIS makes it possible to prepare an energy audit and suggest an energy saving plan based on the diagnosed chilled water supply system.

• Journal of IKEEE
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• v.22 no.4
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• pp.1093-1098
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• 2018

In this paper, we proposed a methodology for portable disease diagnosis platform using high performance computing. The proposed methodology consists of gathering clinical data, diagnosis and feature selection algorithm, implementation of diagnosis platform. For the algorithm verification, a clinical data which is obtained from 401 people(314 normal subjects and 87 liver cancer patients) using a microarray consists of 1,146 aptamers were used. As the result, we could diagnosis liver cancer with 97.5% accuracy using the 32 selected aptamers. Based on these results, we designed and implemented a portable disease diagnosis platform which has 32 bio-signals as inputs.

• Nuclear Engineering and Technology
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• v.46 no.6
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• pp.737-752
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• 2014

As condition-based maintenance (CBM) has risen as a new trend, there has been an active movement to apply information technology for effective implementation of CBM in power plants. This motivation is widespread in operations and maintenance, including monitoring, diagnosis, prognosis, and decision-making on asset management. Thermal efficiency analysis in nuclear power plants (NPPs) is a longstanding concern being updated with new methodologies in an advanced IT environment. It is also a prominent way to differentiate competitiveness in terms of operations and maintenance costs. Although thermal performance tests implemented using industrial codes and standards can provide officially trustworthy results, they are essentially resource-consuming and maybe even a hind-sighted technique rather than a foresighted one, considering their periodicity. Therefore, if more accurate performance monitoring can be achieved using advanced data analysis techniques, we can expect more optimized operations and maintenance. This paper proposes a framework and describes associated methodologies for in-situ thermal performance analysis, which differs from conventional performance monitoring. The methodologies are effective for monitoring, diagnosis, and prognosis in pursuit of CBM. Our enabling techniques cover the intelligent removal of random and systematic errors, deviation detection between a best condition and a currently measured condition, degradation diagnosis using a structured knowledge base, and prognosis for decision-making about maintenance tasks. We also discuss how our new methods can be incorporated with existing performance tests. We provide guidance and directions for developers and end-users interested in in-situ thermal performance management, particularly in NPPs with large steam turbines.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.8
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• pp.1537-1546
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• 2011

The photovoltaic(PV) generation system have recently become widely used to solve the environmental problems and running out of fossil fuels. However, the study on maintenance is inadequate for PV system. This paper proposes the novel diagnosis method of output power decline to maintain the normal output performance of PV array. The diagnosis method used the proportional relation of irradiation-output current(S-I) of PV array at maximum power point(MPP). And, first order polynomial using the relation is proposed to easily apply PV system. To estimate the relation in case of separation of PV array producer and diagnosis system producer. Kalman Filter algorithm is also proposed at 30.2kW grid-connected PV system. Then, the performance of diagnosis method is evaluated using the hardware tests as well as the simulation.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.285-292
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• 2023

With the wide spread of smart farms and the advancements in IoT technology, it is easy to obtain additional data in addition to crop images. Consequently, deep learning-based crop disease diagnosis research utilizing multimodal data has become important. This study proposes a crop disease diagnosis method using multimodal supervised contrastive learning by expanding upon the multimodal self-supervised learning. RandAugment method was used to augment crop image and time series of environment data. These augmented data passed through encoder and projection head for each modality, yielding low-dimensional features. Subsequently, the proposed multimodal supervised contrastive loss helped features from the same class get closer while pushing apart those from different classes. Following this, the pretrained model was fine-tuned for crop disease diagnosis. The visualization of t-SNE result and comparative assessments of crop disease diagnosis performance substantiate that the proposed method has superior performance than multimodal self-supervised learning.

• Knowledge Management Research
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• v.10 no.1
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• pp.71-100
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• 2009

Recently, many of organizations build up their performance measurement system (PMS) to measure their knowledge management performance. However, the system that doesn't well reflect the organization's strategies as well as surroundings could obstruct their performance improvement, instead. Therefore, It is really important to establish the PMS to reflect organization's surroundings and strategies. The purpose of this study is to make a diagnosis of a performance measurement practice of a domestic university's knowledge management. To serve this research purpose, we examine the uptight performance index and PMS from existing references. And we diagnose the specific practices and maturity rates of measuring performances, and the recognition of the performance index at "D" university recently adopting balanced scorecard to performance evaluation through the survey on academic affairs committee members, performance evaluation committee members, and administration members. The method analyzing data from the survey is a gap analysis which includes alignment analysis, congruence analysis, consensus analysis, and confusion analysis. We make a diagnosis of performance measurement practices at "D" university, raise several points of this performance measurement system, and present the improvement plans from these problems.

• The KSFM Journal of Fluid Machinery
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• v.20 no.2
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• pp.32-40
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• 2017

In this study, an in-house program to analyze the performance degradation for gas turbines is developed using MATLAB and is validated using commercial software. This program consists of design and off-design calculations. The results of design calculation is used for reference values of off-design calculation. The off-design calculation is composed of measured and expected performance analyses, and turbine inlet temperature correction. In general, performance degradation is analyzed by comparing the results of measured and expected performance analysis. However, if gas turbine performance degrades, turbine inlet temperature might increase due to the general control logic to comply with the power demand. Therefore, it is required to consider the deviation of turbine inlet temperature from the normal value in the performance diagnosis to analyze the performance degradation exactly. In this study, a special effort is given to the correction of turbine inlet temperature. The accuracy of the developed program is confirmed by comparison with commercial software, and its capability of performance diagnosis using the turbine inlet temperature correction is demonstrated.