• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.9
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• pp.512-519
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• 2000

A Neural networks has been used for a expert system and fault diagnosis system. It is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping.쪼두 a fault occur in system a state of system is changed with transient state. Because of a previous state signal is considered as a information DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.421-428
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• 2012

Analog instrument and control systems in nuclear power plants have recently been replaced with digital systems for safer and more efficient operation. Digital instrument and control systems have adopted various fault-tolerant techniques that help the system correctly and safely perform the specific required functions regardless of the presence of faults. Each fault-tolerant technique has a different inspection period, from real-time monitoring to monthly testing. The range covered by each faulttolerant technique is also different. The digital instrument and control system, therefore, adopts multiple barriers consisting of various fault-tolerant techniques to increase the total fault detection coverage. Even though these fault-tolerant techniques are adopted to ensure and improve the safety of a system, their effects on the system safety have not yet been properly considered in most probabilistic safety analysis models. Therefore, it is necessary to develop an evaluation method that can describe these features of digital instrument and control systems. Several issues must be considered in the fault coverage estimation of a digital instrument and control system, and two of these are addressed in this work. The first is to quantify the fault coverage of each fault-tolerant technique implemented in the system, and the second is to exclude the duplicated effect of fault-tolerant techniques implemented simultaneously at each level of the system's hierarchy, as a fault occurring in a system might be detected by one or more fault-tolerant techniques. For this work, a fault injection experiment was used to obtain the exact relations between faults and multiple barriers of faulttolerant techniques. This experiment was applied to a bistable processor of a reactor protection system.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.1
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• pp.1-5
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• 2011

To enter next process control, numerous approaches, including run-to-run (R2R) process control and fault detection and classification (FDC) have been suggested in semiconductor manufacturing industry as a facilitation of advanced process control. This paper introduces a novel type of optical plasma process monitoring system, called plasma eyes chromatic system (PECSTM) and presents its potential for the purpose of fault detection. Qualitatively comparison of optically acquired signal levels vs. process parameter modifications are successfully demonstrated, and we expect that PECSTM signal can be a useful indication of onset of process change in real-time for advanced process control (APC).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.192-197
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• 2020

Multivariate processes, such as large scale power plants or chemical processes are operated in very hazardous environment, which can lead to significant human and material losses if a fault occurs. On-line monitoring technology, therefore, is essential to detect system faults. In this paper, the ICA-based fault detection method is conducted using three different multivariate process data. Fault detection procedure based on ICA is divided into off-line and on-line processes. The off-line process determines a threshold for fault detection by using the obtained dataset when the system is normal. And the on-line process computes statistics of query vectors measured in real-time. The fault is detected by comparing computed statistics and previously defined threshold. For comparison, the PCA-based fault detection method is also implemented in this paper. Experimental results show that the ICA-based fault detection method detects the system faults earlier and better than the PCA-based method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.9
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• pp.413-422
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• 2002

In a power plant, disturbance detection and diagnosis are massive and complex problems. Once a disturbance occurs, it can be either persistent, self cleared, cleared by the automatic controllers or propagated into another disturbance until it subsides in a new equilibrium or a stable state. In addition to the Physical complexity of the power plant structure itself, these dynamic behaviors of the disturbances further complicate the fault monitoring and diagnosis tasks. A data structure called a disturbance interrelation analysis graph(DIAG) is proposed in this paper, trying to capture, organize and better utilize the vast and interrelated knowledge required for power plant disturbance detection and diagnosis. The DIAG is a multi-layer directed AND/OR graph composed of 4 layers. Each layer includes vertices that represent components, disturbances, conditions and sensors respectively With the implementation of the DIAG, disturbances and their relationships can be conveniently represented and traced with modularized operations. All the cascaded disturbances following an initial triggering disturbance can be diagnosed in the context of that initial disturbance instead of diagnosing each of them as an individual disturbance. DIAG is applied to a typical cooling water system of a thermal power plant and its effectiveness is also demonstrated.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.4
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• pp.880-888
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• 2001

This paper presents a fault detection asnd isolation algorithm for highly reliable gantry crane system. The algorithm is constructed by multiple PI observer technique, and the magnitude of actuator fault can be estimated by using integrated estimated output error. Also, the complex actuator and /or sensor fault can be detected and isolated by monitoring the integrated error and the estimated state error. Considering the actuator and/or the sensor fault, we verify that these fault can be detected and isolated through simulation. Lastly, we show a simple reliable control method by using the detected fault signal and an added observer.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.488-490
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• 1994

To meet the requirements of high performance and reliability as a industrial motor drive, we developed an integrated oil-line fault diagnosis and monitoring system which consists of DSP-based controller and PC-based MMI (Man-machine interface) program. The dedicated controller performs real-lime fault detections and protections. The MMI program monitors the on-line fault status of the drive system and offers full explanations of the fault name(WHAT?), deducible causes of the fault operation(WHY?), and chock points (HOW?) based upon the experiences of the expert. Also the TRACE data which was stored just before and after the accident can be scrutinized using MMI tools.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.835-842
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• 2020

This paper considers a method of reproducing abnormal and fault operation for smart monitoring of thrust bearing used in wave power generation system. In order to develop smart monitoring technology, abnormal and failure data of actual equipment are required. However, it is impossible to artificially break down the actual equipment in operation due to safety and cost. To tackle this problem, a test bed that can secure data through reproduction of a faulty operating environment should be developed. Therefore, in this study, test bed that can reproduce each situation was developed and the operation result was analysis after identifying the situation to be reproduced through the failure factor analysis of the thrust bearing.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.154-159
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• 2012

Condition monitoring(CM) is a method based on Non-destructive test(NDT). Therefore, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days also. Because its sensitivity is higher than normal accelerometers and it can detect low energy vibration signals. A machine tool consist of many parts such as the bearings, gears, process tools, shaft, hydro-system, and so on. Condition of Every part is connected with product quality finally. To increase the quality of products, condition monitoring of the components of machine tool is done completely. Therefore, in this paper, acoustic emission method is used to detect a machine fault seeded in a gearbox. The AE signals is saved, and power spectrums and feature values, peak value, mean value, RMS, skewness, kurtosis and shape factor, were determined through Matlab.

• Current Optics and Photonics
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• v.4 no.3
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• pp.186-192
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• 2020

This paper describes a real-time monitoring system in Ethernet passive optical networks (EPON) that uses burst-mode fiber Bragg grating (FBG) optical sensors. The FBG interrogation unit in the optical line terminal (OLT) transmits the monitoring wavelength to optical network units (ONUs). The FBG sensor unit in each ONU returns a burst-mode monitoring signal to the OLT. As the system applies time division multiple access (TDMA), a uniform Bragg wavelength can be used to monitor the EPON system. The FBG interrogation unit analyzes the received burst-mode monitoring signals and outputs fault information on the ONU branches in EPON. The simulation results show the effectiveness of the proposed monitoring system based on TDMA. In addition, we compared the proposed TDMA-based monitoring system with a WDMA-based monitoring system.