• The Journal of Korean Association of Computer Education
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• v.8 no.5
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• pp.129-137
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• 2005

Criticality prediction models that identify fault-prone spots using system design specifications play an important role in reducing development costs of large systems such as telecommunication systems. Many criticality prediction models using complexity metrics have been suggested. But most of them need training data set for model training. And they are classification models that can only classify design entities into fault-prone group and non fault-prone group. To solve this problem, this paper builds a new prediction model, HMM, using two styled hybrid metrics. HMM has strong point that it does not need training data and it enables comparison between design entities by criticality. HMM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering internal characteristics and accuracy of prediction.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.141-147
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• 2011

This paper proposes an efficient two-stage fault prediction algorithm for fault detection and diagnosis of induction motors. In the first phase, we use a linear predictive coding (LPC) method to extract fault patterns. In the second phase, we use a dynamic time warping (DTW) method to match fault patterns. Experiment results using eight vibration data, which were collected from an induction motor of normal fault states with sampling frequency of 8 kHz and sampling time of 2.2 second, showed that our proposed fault prediction algorithm provides about 45% better accuracy than a conventional fault diagnosis algorithm. In addition, we implemented and tested the proposed fault prediction algorithm on a testbed system including TI's TMS320F2812 DSP that we developed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.1-6
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• 2003

The Delayed Feedback Control method (DFC) proposed by Pyragas applies an input based on the difference between the current state of the system, which is generating chaos orbits, and the $\tau$-time delayed state, and stabilizes the chaos orbit into a target. In DFC, the information about a position in the state space is unnecessary if the period of the unstable periodic orbit to stabilize is known. There exists the fault that DFC cannot stabilize the unstable periodic orbit when a linearlized system around the periodic point has an odd number property. There is the chaos control method using the prediction of the $\tau$-time future state (PDFC) proposed by Ushio et al. as the method to compensate this fault. Then, we propose a method such as improving the fault of the DFC. Namely, we combine DFC and PDFC with parameter W, which indicates the balance of both methods, not to lose each advantage. Therefore, we stabilize the state into the $\tau$ periodic orbit, and ask for the ranges of Wand gain K using Jury' method, and determine the quasi-optimum pair of (W, K) using a genetic algorithm. Finally, we apply the proposed method to a discrete-time chaotic system, and show the efficiency through some examples of numerical experiments.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.3
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• pp.199-205
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• 2011

This paper proposes a framework to predict the mechanical fault of hot rolling mill system (HRMS). The optimum process of HRMS is usually identified by the rotating velocity of working roll. Therefore, observing the velocity of working roll is relevant to early know the HRMS condition. In this paper, we propose the framework which consists of two methods namely spectrum matrix which related to case-based fast Fourier transform(FFT) analysis, and three dimensional condition monitoring based on novel visualization. Validation of the proposed method has been conducted using vibration data acquired from HRMS by accelerometer sensors. The acquired data was also tested by developed software referred as hot rolling mill facility analysis module. The result is plausible and promising, and the developed software will be enhanced to be capable in prediction of remaining useful life of HRMS.

• International Journal of Control, Automation, and Systems
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• v.4 no.1
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• pp.105-112
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• 2006

The main function of a reactor protection system is to maintain the reactor core integrity and the reactor coolant system pressure boundary. Generally, the reactor protection system adopts the 2-out-of-m redundant architecture to assure a reliable operation. This paper describes the safety assessment of a digital reactor protection system using the fault tree analysis technique. The fault tree technique can be expressed in terms of combinations of the basic event failures such as the random hardware failures, common cause failures, operator errors, and the fault tolerance mechanisms implemented in the reactor protection system. In this paper, a prediction method of the hardware failure rate is suggested for a digital reactor protection system, and applied to the reactor protection system being developed in Korea to identify design weak points from a safety point of view.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.27 no.6
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• pp.391-405
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• 2015

A global tsunami prediction system was suggested for a distant tsunami using a finite fault model and a cyclic boundary condition. The possibility of the suggested system as a distant tsunami response system was checked by applying it into the case of 2014 Chile tsunami. A comparison between the numerical results(tsunami height and arrival time) with different conditions (boundary condition, governing equation, grid size and fault model) and measured data (DART buoy, tide station) showed the importance of the finite fault model and the cyclic boundary condition.

• Earthquakes and Structures
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• v.22 no.1
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• pp.53-64
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• 2022

This study proposes an intelligent semi-active isolation system combining a variable-stiffness control device and ground motion characteristic prediction. To determine the optimal control parameter in real-time, a genetic algorithm (GA)-fuzzy control law was developed in this study. Data on various types of ground motions were collected, and the ground motion characteristics were quantified to derive a near-fault (NF) characteristic ratio by employing an on-site earthquake early warning system. On the basis of the peak ground acceleration (PGA) and the derived NF ratio, a fuzzy inference system (FIS) was developed. The control parameters were optimized using a GA. To support continuity under near-fault and far-field ground motions, the optimal control parameter was linked with the predicted PGA and NF ratio through the FIS. The GA-fuzzy law was then compared with other control laws to verify its effectiveness. The results revealed that the GA-fuzzy control law could reliably predict different ground motion characteristics for real-time control because of the high sensitivity of its control parameter to the ground motion characteristics. Even under near-fault and far-field ground motions, the GA-fuzzy control law outperformed the FPEEA control law in terms of controlling the isolation layer displacement and the superstructure acceleration.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.5
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• pp.60-67
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• 2014

Wind power is one of the fastest growing renewable energy sources. The blades length and tower height of wind turbine have been growing steadily in the last 10 years in order to increase the output amount of wind power energy. The amount of wind turbine energy is increased by increasing the capacity of wind turbine, but the costs of preventive, corrective and replacement maintenance are also increased accordingly. Recently, Condition Monitoring System that can repair the fault diagnose and repair of wind turbine in the real-time. However, these system have a problem that cannot predict and diagnose of the fault. In this paper, wind turbine predict methodology is proposed by using the SVM method. In the case study, correlation analysis between wind turbine fault and external environmental factors is performed by using the SVM method.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.81-83
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• 2005

The Reactor Protection System (RPS) is a very important system in a nuclear power plant because the system shuts down the reactor to maintain the reactor core integrity and the reactor coolant system pressure boundary if the plant conditions approach the specified safety limits. This paper describes the unavailability assessment of a digital reactor protection system using the fault tree analysis technique. The fault tree technique can be expressed in terms of combinations of the basic event failures. In this paper, a prediction method of the hardware failure rate is suggested for a digital reactor protection system. and applied to the reactor protection system being developed in Korea.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1213-1216
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• 1998

This paper presents the result of a feasibility study for the prediction method of fault symptoms on 22.9kV distribution line. In this paper, real distribution data was collected and analyzed to isolate failure signatures or parameters which were distinct behaviors before and after failure incident. A new strategy of analysis-based (event-date concept) prediction algorithm for the distribution insulators and a developed model system were also discussed.