• Title/Summary/Keyword: a on-line fault diagnosis

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A Study on the Interface between DAS and SCADA by using Direct Method (직접연계방법에 의한 DAS-SCADA 연계 연구)

  • Park, So-Young;Shin, Chang-Hoon
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.9 no.5
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    • pp.1154-1163
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    • 2008
  • DAS and SCADA system are main systems, which operate and manage the power system by using computer and communication technology. But DAS and SCADA have been operated independently in Korea, so there was some time delay in the fault handling and live-line work. To improve that problems, studies on the interface between DAS and SCADA have been needed. From November 2005 to December 2007, the pilot project has been performed in KEPCO's branch offices, as a result, direct method will be applied widely all over the country until 2010 because the operation information of substation users want can be transmitted fast and it is possible to control 43RC directly although the expense for direct method is about 1.3 times larger than that for web method. To improve problems revealed during the pilot project, substation object to object communication method is proposed and interface database automatic generation and interface system diagnosis and logging functions are developed. Interface data point and communication data type are defined, substation single line diagram automatic generation or manual editing function, SCADA monitoring and controlling function, log data managing function are developed in SCADA HMI program. It is expecting to reduce the average time for each outage about 33% and to improve the reliability, efficiency of operation, execution of DAS from automatic load transfer under a fault, and to contribute to export project.

A Conceptual Design of Maintenance Information System Interlace for Real-Time Diagnosis of Driverless EMU (무인전동차의 실시간 상태 진단을 위한 유지보수 정보시스템 인터페이스에 대한 개념설계)

  • Han, Jun-hee;Kim, Chul-Su
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.18 no.10
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    • pp.63-68
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    • 2017
  • Although automated metro subway systems have the advantage of operating a train without a train driver, it is difficult to detect an immediate fault condition and take countermeasures when an unusual situation occurs. Therefore, it is important to construct a maintenance information system (MIS) that detects the vehicle failure/status information in real time and maintains it efficiently in the depot of the railway's vehicles. This paper proposes a conceptual design method that realizes the interface between the train control system (TCS), the operation control center train control monitoring system (OCC-TCMS) console, and the MIS using wireless communication network in real-time. To transmit a large amount of information on 800,000 occurrences per day during operation, data was collected in a 56 byte data table using a data processing algorithm. This state information was classified into 4 hexadecimal codes and transmitted to the MIS by mapping the status and the fault information on the vehicle during the main line operation. Furthermore, the transmission and reception data were examined in real time between the TCS and MIS, and the implementation of the failure information screen was then displayed.

Research on rapid source term estimation in nuclear accident emergency decision for pressurized water reactor based on Bayesian network

  • Wu, Guohua;Tong, Jiejuan;Zhang, Liguo;Yuan, Diping;Xiao, Yiqing
    • Nuclear Engineering and Technology
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    • v.53 no.8
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    • pp.2534-2546
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    • 2021
  • Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.

An Intelligence Support System Research on KTX Rolling Stock Failure Using Case-based Reasoning and Text Mining (사례기반추론과 텍스트마이닝 기법을 활용한 KTX 차량고장 지능형 조치지원시스템 연구)

  • Lee, Hyung Il;Kim, Jong Woo
    • Journal of Intelligence and Information Systems
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    • v.26 no.1
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    • pp.47-73
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    • 2020
  • KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.