• E2M - 전기 전자와 첨단 소재
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• 제10권4호
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• pp.349-354
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• 1997

This paper aims at studying the improvement of test method for t-weight sensitive fault (t-wsf) detect. The development of RAM fabrication technology results in not only the increase at device density on chips but also the decrease in line widths in VLSI. But, the chip size that was large and complex is shortened and simplified while the cost of chips remains at the present level, in many cases, even lowering. First of all, The testing patterns for RAM fault detect, which is apt to be complicated , need to be simplified. This new testing method made use of Local Lower Bound (L.L.B) which has the memory with the beginning pattern of 0(l) and the finishing pattern of 0(1). The proposed testing patterns can detect all of RAM faults which contain stuck-at faults, coupling faults. The number of operation is 6N at 1-weight sensitive fault, 9,5N at 2-weight sensitive fault, 7N at 3-weight sensitive fault, and 3N at 4-weight sensitive fault. This test techniques can reduce the number of test pattern in memory cells, saving much more time in test, This testing patterns can detect all static weight sensitive faults and pattern sensitive faults in RAM.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2357-2360
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• 2002

For the satisfying performance of a oil lubricator, design of a oil controller for the system which meets the required specifications and its supporting hardware that keep their functioning is important. Among the hardware of a control system, oil system are most vulnerable to malfunction. Thus it is necessary to keep track of accurate and reliable oil readings for good fusion oil lubricator performance. In case of oil lubricator, data loss, ssr trigger error faults, they are detected by examining the data system output values and the major values of the system, and then the faults are recognized by the analysis of symptoms of faults. If necessary electronic-sensor values are synthesized according to the types of faults, and then they are used for the controller instead of the raw data. In this paper, a fast-32bit cpu microprocessor applied to the control of flexible link system with the sensor fault problems in the error modulo for exact positioning to show the applicability. It is shown that the fusion oil lubricator can provide a satisfactory loop performance even when the sensor faults occure.

• Journal of Mechanical Science and Technology
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• 제19권8호
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• pp.1597-1610
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• 2005

The conventional computerized numerical controller (CNC) of machine tools has been increasingly replaced by a PC-based open architecture CNC (OAC) that is independent of a CNC vendor. The OAC and machine tools with an OAC have led to a convenient environment in which user-defined applications can be efficiently implemented within a CNC. This paper proposes a method of diagnosing the cause of operational faults. The method is based on the status of a programmable logic controller in machine tools with an OAC. An operational fault is defined as a disability that occurs during the normal operation of machine tools. Operational faults constitute more than 70 percent of all faults and are also unpredictable because most of them occur without any warning. To quickly and correctly diagnose the cause of an operational fault, two diagnostic models are proposed: the switching function and the step switching function. The cause of the fault is logically diagnosed through a fault diagnosis system using diagnostic models. A suitable interface environment between a CNC and developed application modules is constructed to implement the diagnostic functions in the CNC domain. The results of the diagnosis were displayed on a CNC monitor for machine operators and transmitted to a remote site through a Web browser. The proposed diagnostic method and its results were useful to unskilled machine operators and reduced the machine downtime.

• 전기학회논문지
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• 제61권12호
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• pp.1953-1958
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• 2012

The major causes of electrical fire are classified to short circuit fault, overload fault, electric leakage and electric contact failure. The occurrence factor of the fire is electric arc or spark accompanied with such electric faults, specially short circuit faults. Earth Leakage Circuit Breaker (ELB) and Molded_case Circuit Breaker (MCCB), that is, Residual Current Protective Devices (RCDs) used on low voltage distribution lines cut off earth leakage and overload, but the RCD can not cut off electric arc or spark to be a major factor of electrical fire. As the RCDs which are applied in low voltage distribution panel are prescribed to rated breaking time about 30ms(KS C 4613), the RCDs can't perceive to the periodic electric arc or spark of more short wavelength level. To improve such problem, this paper proposes a prevention apparatus using the line voltage drop of neutral wire and some semiconductor switching devices. Some experimental tests of the proposed apparatus confirm the validity of the analytical results.

• Journal of Electrical Engineering and Technology
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• 제7권3호
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• pp.411-419
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• 2012

This paper proposes a new FDI(Fault Detection and Isolation) method, which is called EPSA(Extended Parity Space Approach). This method is particularly suitable for fault detection and isolation of the system with one faulty sensor or two faulty sensors. In the system with two faulty sensors, the fault detection and isolation probability may be decreased when two faults are occurred between the sensors related to the large fault direction angle. Nonetheless, the previously suggested FDI methods to treat the two-faults problem do not consider the effect of the large fault direction angle. In order to solve this problem, this paper analyzes the effect of the large fault direction angle and proposes how to increase the fault detection and isolation probability. For the increase the detection probability, this paper additionally considers the fault type that is not detected because of the cancellation of the fault biases by the large fault direction angle. Also for the increase the isolation probability, this paper suggests the additional isolation procedure in case of two-faults. EPSA helps that the user can know the exact fault situation. The proposed FDI method is verified through Monte Carlo simulation.

• Journal of Electrical Engineering and Technology
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• 제4권1호
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• pp.28-34
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• 2009

In a Gas Insulated Substation (GIS), Very Fast Transients (VFTs) are generated mainly due to switching operations. These transients may cause internal faults, i.e., layer-to-layer faults in a capacitively graded bushing as it is one of the most important terminal equipment for GIS. The healthiness of the bushing is generally verified by measuring its leakage current. However, the change in current magnitude/pattern is only marginal for different types of fault conditions. Leakage current monitoring (LCM) systems generate large amounts of data and computer aided interpretation of defects may be of great assistance when analyzing this data. In view of the above, ANN techniques have been used in this study for identification of these minor faults. A single layer perceptron network, a two layer feed-forward back propagation network and cascade correlation (CC) network models are used to identify interlayer faults in the bushing. The effectiveness of the CC network over perceptron and back propagation networks in identification of a fault has been analysed as part of the paper.

• Journal of Electrical Engineering and Technology
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• 제13권4호
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• pp.1614-1622
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• 2018

The research related to fault diagnosis in permanent magnet synchronous motors (PMSMs) has attracted considerable attention in recent years because various faults such as permanent magnet demagnetization and short-circuited turns can occur and result in unexpected failure of motor related system. Several conventional current and back electromotive force (BEMF) analysis techniques were proposed to detect certain faults in PMSMs; however, they generally deal with a single fault only. On the contrary, cases of multiple faults are common in PMSMs. We propose a fault diagnosis method for PMSMs with single and multiple combined faults. Our method uses three phase BEMF voltages based on the fast Fourier transform (FFT), support vector machine(SVM), and visualization tools for identifying fault types and severities in PMSMs. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) are used to visualize the high-dimensional data into two-dimensional space. Experimental results show good visualization performance and high classification accuracy to identify fault types and severities for single and multiple faults in PMSMs.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2002년도 춘계합동학술대회 논문집
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• pp.137-140
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• 2002

For the satisfying performance of a oil lubricator, design of a oil controller for the system which meets the required specifications and its supporting hardware that keep their functioning is important. Among the hardware of a control system, oil system are most vulnerable to malfunction. Thus it is necessary to keep track of accurate and reliable oil readings for good fusion oil lubricator performance. In case of oil lubricator, data loss, ssr trigger error faults, they are detected by examining the data system output values and the major values of the system, and then the faults are recognized by the analysis of symptoms of faults. If necessary electronic-sensor values are synthesized according to the types of faults, and then they are used for the controller instead of the raw data. In this paper, a fast-32bit cpu micorprocessor applied to the control of flexible link system with the sensor fault problems in the error module for exact positioning to show the applicability. It is shown that the fusion oil lubricator can provide a satisfactory loop performance even when the sensor faults occure

• Journal of Power Electronics
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• 제8권2호
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• pp.181-191
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• 2008

The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.

• Nuclear Engineering and Technology
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• 제50권8호
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.