• ETRI Journal
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• v.31 no.2
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• pp.209-214
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• 2009

In this paper, we propose a novel test methodology for the detection of catastrophic and parametric faults present in analog very large scale integration circuits. An automatic test pattern generation algorithm is proposed to generate piece-wise linear (PWL) stimulus using wavelets and a genetic algorithm. The PWL stimulus generated by the test algorithm is used as a test stimulus to the circuit under test. Faults are injected to the circuit under test and the wavelet coefficients obtained from the output response of the circuit. These coefficients are used to train the neural network for fault detection. The proposed method is validated with two IEEE benchmark circuits, namely, an operational amplifier and a state variable filter. This method gives 100% fault coverage for both catastrophic and parametric faults in these circuits.

• Journal of the Korean Society of Safety
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• v.19 no.1
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• pp.23-30
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• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.963-968
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• 2003

The continuous process systems usually consists of various components: driven rollers. idle rolls, load-cell and so on. Even a simple fault in a single component in the line may cause a catastrophic damage on the final products. Therefore it is absolutely necessary to diagnosis the components of the continuous systems. In this paper, an adaptive eccentricity compensation method is presented. And a new diagnosis method for transverse roll shape defects on rolling process is developed. The new method was induced from analyzing the rolling mechanism by using rolling force model, tension model, Hitchcock's equation, and measured delivery thickness of materials etc. Computer simulation results also show that the proposed diagnosis methods is very effective in the diagnosis of 3-D roll shape

• ETRI Journal
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• v.30 no.4
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• pp.546-554
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• 2008

Phase-locked loops (PLLs) are among the most important mixed-signal building blocks of modern communication and control circuits, where they are used for frequency and phase synchronization, modulation, and demodulation as well as frequency synthesis. The growing popularity of PLLs has increased the need to test these devices during prototyping and production. The problem of distinguishing and classifying the responses of analog integrated circuits containing catastrophic faults has aroused recent interest. This is because most analog and mixed signal circuits are tested by their functionality, which is both time consuming and expensive. The problem is made more difficult when parametric variations are taken into account. Hence, statistical methods and techniques can be employed to automate fault classification. As a possible solution, we use the back propagation neural network (BPNN) to classify the faults in the designed charge-pump PLL. In order to classify the faults, the BPNN was trained with various training algorithms and their performance for the test structure was analyzed. The proposed method of fault classification gave fault coverage of 99.58%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.449-455
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• 2004

This paper presents a novel defect detection method for one chip RF front end with fault detection circuits using input matching measurement. We present a BIST circuit using 40.25{\mu}m$ CMOS technology. We monitor the input transient voltage of the RF front end to differentiate faulty and fault-free RF front end. Catastrophic as well as parametric variation fault models are used to simulate the faulty response of the RF front end. This technique has several advantages with respect to the standard approach based on current test stimulus and frequency domain measurement. Because DUT and fault detection circuits are implemented in the same chip, this test technique only requires use of digital voltmeter (RMS meter) and RF voltage source generator for simpleand inexpensive testing.

• Nuclear Engineering and Technology
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• v.36 no.3
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• pp.210-218
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• 2004

In the interest of nuclear power plant safety, a self-diagnostic monitoring system (SDMS) is needed to monitor defects in safety-related components. An air-operated valve (AOV) is one of the components to be monitored since the failure of its operation could potentially have catastrophic consequences. In this paper, a model of the AOV is developed with the parameters that affect the operational characteristics. The model is useful for both understanding the operation and correlating parameters and defects. Various defects are introduced in the experiments to construct a fault library, which will be used in a pattern recognition approach. Finally, the validity of the fault library is examined.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1213-1220
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• 2022

The Emergency Diesel Generator (EDG) is a critical and essential part of the Nuclear Power Plant (NPP). Due to past catastrophic disasters, critical systems of NPP like EDG are designed to meet high dependability requirements. Therefore, we propose a framework for the dynamic reliability assessment using the Fault Tree and the Dynamic Bayesian Network. In this framework, the information of the component's failure probability is updated based on observed data. The framework is powerful to perform qualitative as well as quantitative analysis of the system. The validity of the framework is done by applying it on several NPP systems.

• Smart Structures and Systems
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• v.16 no.2
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• pp.329-345
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• 2015

As commonly known, sensor errors and faulty signals may potentially lead structures in vibration to catastrophic failures. This paper presents a new approach to deal with sensor errors/faults in vibration control of structures by using the Fault detection and isolation (FDI) technique. To demonstrate the effectiveness of the approach, a space truss structure with semi-active devices such as Magneto-Rheological (MR) damper is used as an example. To address the problem, a Linear Matrix Inequality (LMI) based fixed-order $H_{\infty}$ FDI filter is introduced and designed. Modeling errors are treated as uncertainties in the FDI filter design to verify the robustness of the proposed FDI filter. Furthermore, an innovative Fuzzy Fault Tolerant Controller (FFTC) has been developed for this space truss structure model to preserve the pre-specified performance in the presence of sensor errors or faults. Simulation results have demonstrated that the proposed FDI filter is capable of detecting and isolating sensor errors/faults and actuator faults e.g., accelerometers and MR dampers, and the proposed FFTC can maintain the structural vibration suppression in faulty conditions.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.638-645
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• 2007

Computers are increasingly being introduced into safety and reliability critical systems. The safe and reliable operation of these systems cannot be taken for granted. Malfunctions of these systems can have potentially catastrophic consequences and they have already been involved in serious accidents. Software fault prevention, fault tolerance, fault removal and fault forecasting are the techniques to be used, implemented and verified for embedded software in critical systems as the contributors to safety and reliability of the software. To use them when developing a software product, a relationship must be established between them and the development processes, the methods and techniques to be used to develop software, as well as with the different product architectures. Railroad signaling system software is a safety-critical embedded software with realtime and high reliability requirements. The primary purpose of the safety management is to prevent the loss of lives or physical damages arising from potential hazards in the railroad signaling system. This study provides a systematic approach to analysis of potential hazards for their management during the system life cycle to assure the identification and definition of the most appropriate hazards.

• Progress in Superconductivity and Cryogenics
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• v.14 no.2
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• pp.12-15
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• 2012

This paper presents simulation and small-scale experimental tests of a fault current controller. Smart fault controller as proposed and proven conceptually in our previous work is promising technology for the smart power grid where distributed and even stochastic generation sources are prevalent and grid operations are more dynamic. Existing protection schemes simply limiting the fault current to the pre-determined set values may not show best performance and even lead to coordination failures, potentially leading to catastrophic failure. Thus, this paper designs fault current controller with a full bridge thyristor rectifier, embedding a superconducting coil for which the controller is electrically invisible during normal operation because the loss due to the coil is near-zero. When a fault occurs and the resulting current through the superconducting coil exceeds a certain value set intelligently based on the current operating condition of the grid, the magnitude of the fault current is controlled to this desired value by adjusting the firing angles of thyristors such that the overall system integrity is successfully maintained. Detailed time-domain simulations are performed and lab-scale testing circuits are built to demonstrate the desired functionality and efficacy of the proposed fault current controller.