• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1479-1501
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• 2020

Dependable functioning of dynamic networks is essential for delivering ubiquitous services. Faults are the root causes of network outages. The comparison diagnosis model, which automates fault's identification, is one of the leading approaches to attain network dependability. Most of the existing research has focused on stationary networks. Nonetheless, the time-free comparison model imposes no time constraints on the system under considerations, and it suits most of the diagnosis requirements of dynamic networks. This paper presents a novel protocol that diagnoses faulty nodes in diagnosable dynamic networks. The proposed protocol comprises two stages, a testing stage, which uses the time-free comparison model to diagnose faulty neighbour nodes, and a disseminating stage, which leverages a Random Linear Network Coding (RLNC) technique to disseminate the partial view of nodes. We analysed and evaluated the performance of the proposed protocol under various scenarios, considering two metrics: communication overhead and diagnosis time. The simulation results revealed that the proposed protocol diagnoses different types of faults in dynamic networks. Compared with most related protocols, our proposed protocol has very low communication overhead and diagnosis time. These results demonstrated that the proposed protocol is energy-efficient, scalable, and robust.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2488-2491
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• 2000

A Neural network 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. When 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.

• Journal of Power Electronics
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• v.17 no.3
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• pp.725-732
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• 2017

This paper proposed a real-time, low-cost, fast transistor open-circuit fault diagnosis method for voltage-source inverter fed induction motors. A transistor open-circuit changes the symmetry of the inverter topology, leading to different similarities among three phase load currents. In this paper, dynamic time warping is proposed to describe the similarities among load currents. The proposed diagnosis is independent of the system model and needs no extra sensors or electrical circuits. Both simulation and experimental results show the high efficiency of the proposed fault diagnosis method.

• Korean Journal of Oriental Medicine
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• v.14 no.3
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• pp.103-111
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• 2008

Recently, skin diagnosis has been suggested as a promising tool for discrimination of Sasang Constitution, reported by examining the skin characteristics such as thickness, stiffness, slip, and skin textures like wrinkles and furrows. However, the works had a limitation in that clinical decision on the skin characteristics was made by relying upon oriental medicine doctors' subjective sense of touch. In order to objectify the skin diagnosis and claim its efficacy on the discrimination of the Sasang Constitutions, it is necessary to demonstrate its discrimination capability by providing numerical values in terms of physical quantities obtained from measurements using today's sensors and equipment technologies, which motivated this work as a priliminary step towards objectification of skin diagnosis. The skin characteristics focused in this work is the slip property of the back-hand skin that has been exploited using the dynamic friction measurement system. First, curved geometric effects of the back-hand skin on the measured lateral/vertical force signals were estimated using the artificially designed silicon coated structures, which led to a suggestion on a quality controlled experimental design based upon a empirical analysis model. Second, the experimental design thus suggested has been applied to the measurement of dynamic friction coefficients for two healthy male subjects of Taeumin (TE) and Soyangin (SY), respectively. The result shows that the dynamic friction coefficient is less for the SY subject than for the TE subject around the area of the skin used for diagnosis by the oriental medicine doctor, implying the TE subject's skin is more slippery than the SE subject's that is consistent with the oriental medicine doctor's diagnosis. Hopefully, this work can provide guidelines for obtaining quality data in friction measurement to be collected for discussion on the efficacy of the skin diagnosis and its objectification through statistical analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.159-167
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• 2003

This study suggests the hybrid fault diagnosis method of signed digraph (SDG) and partial least squares (PLS). SDG offers a simple and graphical representation for the causal relationships between process variables. The proposed method is based on SDG to utilize the advantage that the model building needs less information than other methods and can be performed automatically. PLS model is built on local cause-effect relationships of each variable in SDG. In addition to the current values of cause variables, the past values of cause and effect variables are inputted to PLS model to represent the Process armies. The measured value and predicted one by dynamic PLS are compared to diagnose the fault. The diagnosis example of CSTR shows the proposed method improves diagnosis resolution and facilitates diagnosis of masked multiple-fault.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1031-1053
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• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.883-887
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• 2002

We should maintain the maximum operation capacity for production facilities and find properly out the fault of each equipment rapidly in order to decrease a loss caused by its failure. The acoustic and vibration signals of a machine always carry the dynamic information of the machine. These signals are very useful fur the feature extraction and fault diagnosis. We performed a fundamental study which develops a system of fault diagnosis for a pump. We experimented vibrations by acceleration sensors and noises by microphones, compared and analysed for normal products, artificially deformed products. We tried to search a change of the dynamic signals according to machine malfunctions and analyse the type of deformation or failure. The results showed that acoustic signals as well as vibration signals can be used as a simple method lot a detection of machine malfunction or fault diagnosis.

• Journal of the Korea Safety Management & Science
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• v.6 no.3
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• pp.303-309
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• 2004

Many system devices for fluoroscopic and general X-ray studies in diagnostic radiographic system have been being changed from analog mode to digital mode. In addition, among diagnostic imaging and radiologic examinations, fluoroscopic studies that requires functional diagnosis is being widely used. The video recording method of fluoroscopic studies has been useful in functional image diagnosis and dynamic image observation, but the utility of its image quality is being reduced because of limitation in setting play segments of the video player, inconvenience of play, difficulties in preserving reproduced images, the change of image quality, etc. In order to complement these shortages, it is necessary to facilitate access to patient diagnosis information such as storing, editing and sharing functional diagnosis images in response to the trend of the digitalization of digital radiographic & fluoroscopic system(DRF). Thus this study designed and implemented a device of storing functional dynamic images real time using a computer rather than existing video recording, aiming at contribution to functional image diagnosis.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.714-717
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• 1996

This paper presents a fault detection and diagnosis methodologies based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. At the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model(WSM) is used to generate those candidates. The weight is determined from dynamic simulation. Using WSMs, the methodology can generate the cause candidates and rank them according to the probability. Secondly, the fault propagation trends identified from the partial or complete sequence of measurements are compared to the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies and showed satisfactory diagnostic resolution.