• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3620-3630
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• 2022

The diagnosis of abnormalities in a nuclear power plant is essential to maintain power plant safety. When an abnormal event occurs, the operator diagnoses the event and selects the appropriate abnormal operating procedures and sub-procedures to implement the necessary measures. To support this, abnormality diagnosis systems using data-driven methods such as artificial neural networks and convolutional neural networks have been developed. However, data-driven models cannot always guarantee an accurate diagnosis because they cannot simulate all possible abnormal events. Therefore, abnormality diagnosis systems should be able to detect their own potential misdiagnosis. This paper proposes a rulebased diagnostic validation algorithm using a previously developed two-stage diagnosis model in abnormal situations. We analyzed the diagnostic results of the sub-procedure stage when the first diagnostic results were inaccurate and derived a rule to filter the inconsistent sub-procedure diagnostic results, which may be inaccurate diagnoses. In a case study, two abnormality diagnosis models were built using gated recurrent units and long short-term memory cells, and consistency checks on the diagnostic results from both models were performed to detect any inconsistencies. Based on this, a re-diagnosis was performed to select the label of the second-best value in the first diagnosis, after which the diagnosis accuracy increased. That is, the model proposed in this study made it possible to detect diagnostic failures by the developed consistency check of the sub-procedure diagnostic results. The consistency check process has the advantage that the operator can review the results and increase the diagnosis success rate by performing additional re-diagnoses. The developed model is expected to have increased applicability as an operator support system in terms of selecting the appropriate AOPs and sub-procedures with re-diagnosis, thereby further increasing abnormal event diagnostic accuracy.

• Journal of Korean Society for Quality Management
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• v.52 no.2
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• pp.357-376
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• 2024

Purpose: The purpose of this study is to design an objective and comparable diagnostic system for diagnosing the technology level of military maintenance depots and verify its actual applicability. Methods: Literature Review, Capability Maturity Model Integration, Analytic Hierarchy Process. Results: Military maintenance depot maintenance quality level diagnosis items, Maintenance quality level by maintenance technology area, Guidelines for diagnosing maintenance quality level, Quality level comparison results by area and implications for improvement. Conclusion: In order to systematically evaluate the maintenance quality of military maintenance depots, this study was conducted with the goal of designing an overall maintenance quality diagnosis system, including diagnosis areas, diagnosis items, and a diagnosis score award system, by improving the existing evaluation method. In addition, the newly developed maintenance quality diagnosis system was applied to actual evaluation activities and the results were returned to members, confirming the usefulness of the developed maintenance quality diagnosis system in the field.

• Proceedings of the KIEE Conference
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• 2004.05b
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• pp.85-87
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• 2004

We described the trend of diagnosis of technology in this paper Diagnosing key technologies in NTRM were explained. Development history of maintenance technology was described and the roles of diagnosis technology for maintenance technology were analyzed from past time to future.

• Korean System Dynamics Review
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• v.17 no.3
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• pp.145-173
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• 2016

This study aims to develop strategic diagnosis framework of performance by identifying and analysing the dynamics of the technology commercialization ecosystem in innovative region. To achieve the purpose of this study, the systems thinking approach is used. The systems thinking approach connects feedback structure and behavior more explicitly to diagnosis vicious feedback loop in the regional technology commercialization ecosystem. In terms of an ecological point of view, it will be possible to explore dominant feedback structure and find leverages to overcome the limitations of regional technology commercialization performance. The diagnosis of reenforcing and balancing feedback structure is based on the statistical analysis of the survey data which has been collected in a cluster random sampling method, targeting on the 200 firm located in the Pangyo and Daeduk region. The results from this research showed that the regional technology commercialization ecosystem was immature and faced limit to the growth. An important finding of this study was that regional technology commercialization ecosystem need to activation of startups and reinforcement of virtuous feedback structures of technology commercialization market systems.

• Smart Structures and Systems
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• v.6 no.4
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• pp.389-403
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• 2010

By using image recognition technology, this paper presents a new fault diagnosis method for rotating machinery with artificial immune algorithm. This method focuses on the vibration state parameter image. The main contribution of this paper is as follows: firstly, 3-D spectrum is created with raw vibrating signals. Secondly, feature information in the state parameter image of rotating machinery is extracted by using Wavelet Packet transformation. Finally, artificial immune algorithm is adopted to diagnose rotating machinery fault. On the modeling of 600MW turbine experimental bench, rotor's normal rate, fault of unbalance, misalignment and bearing pedestal looseness are being examined. It's demonstrated from the diagnosis example of rotating machinery that the proposed method can improve the accuracy rate and diagnosis system robust quality effectively.

• Proceedings of the KIEE Conference
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• 2005.05b
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• pp.7-9
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• 2005

The rotating machine diagnosis technology is very important techniques to secure reliability of facility operation and life extension of operation to rotating machines that are exposed in danger of the insulation deterioration. The rotating machine diagnosis technology road map minimizes economic losses according to the unpredictable accidents of the rotating machine diagnosis technology. As technology is secured, it strengthen the competitiveness and the diagnosis technology road maps will be realizing the technique independence and applying industrial sites

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.321-325
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• 2005

These days, it is needed that more stability and reliability of Diesel engine. So it is essential that a systematic and comprehensive fault diagnosis analysis technology. this technology makes fault diagnosis analysis system more efficient. Expert System is required to make fault diagnosis analysis system. In this paper, fault and early diagnosis system is implemented to use Expert System development tools.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.90.1-90.1
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• 2011

This technology is applicable to Electrical vehicle that using Energy from Hydrogen Fueled Cell. Electricity & water is got from chemical reaction between H2 & O2 in stack. This technology is used when fault diagnosis of Fuel cell is needed. It is General method that measure each cell's voltage of stack for fault diagnosis. but, this technology is method of measuring entire voltage of stack. For this reason, fault diagnosis system is simplified and cost of system is lower than previous one. In normal stack condition, characteristic graph of voltage-current has linearity. In fault stack condition, it has non-linearity. we use this characteristic to diagnosis of stack fault. In this technology, Specific frequency current is injected into stack & Stack voltage is measured in response. After that, stack voltage difference is analyzed to diagnosis of stack fault. Presently, Development of current injection module & basic program of THDA is finished. in future we will develop the technology of precise measurement technology about entire stack voltage.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.154-157
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• 2003

In order for testing faults of combinatorial logic circuit, the authors have developed a new diagnosis method: "Neural Network (NN) fault diagnosis", based on fm error back propagation functions. This method has proved the capability to test gate faults of wider range including so called SSA (single stuck-at) faults, without assuming neither any set of test data nor diagnosis dictionaries. In this paper, it is further shown that what kind of fault models can be detected in the NN fault diagnosis, and the simply modified one can extend to test delay faults, e.g. logic hazard as long as the delays are confined to those due to gates, not to signal lines.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.870-881
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• 2020

Because SCADA monitoring data of wind turbines are large and fast changing, the unbalanced proportion of data in various working conditions makes it difficult to process fault feature data. The existing methods mainly introduce new and non-repeating instances by interpolating adjacent minority samples. In order to overcome the shortcomings of these methods which does not consider boundary conditions in balancing data, an improved over-sampling balancing algorithm SC-SMOTE (safe circle synthetic minority oversampling technology) is proposed to optimize data sets. Then, for the balanced data sets, a fault diagnosis method based on improved k-nearest neighbors (kNN) classification for wind turbine blade icing is adopted. Compared with the SMOTE algorithm, the experimental results show that the method is effective in the diagnosis of fan blade icing fault and improves the accuracy of diagnosis.