• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.4
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• pp.175-182
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• 2002

This paper proposes an analytic method for measuring the accurate fundamental frequency component of a fault current signal distorted with a DC-offset, a characteristic frequency component, and harmonics. The proposed algorithm is composed of four stages: sine filer, linear filter, Prony's method, and measurement. The sine filter and the linear filter eliminate harmonics and the fundamental frequency component, respectively. Then Prony's method is used to estimate the parameters of the DC-offset and the characteristic frequency component. Finally, the fundamental frequency component is measured by compensating the sine-filtered signal with the estimated parameters. The performance evaluation of the proposed method is presented for a-phase to around faults on a 345 kV 200 km overhead transmission line. The EMTP is used to generate fault current signals under different fault locations and fault inception angles. It is shown that the analytic method accurately measures the fundamental frequency component regardless of the characteristic frequency component as well as the DC-offset.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.6
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• pp.791-796
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• 2007

When 2 sets of PID controllers are coupled directly each other to configure a closed control loop on behalf of coupling a controller and a plant. the behaviors or this exclusive loop system are expected to be unique in inherent system responses. If its properties be disclosed and generalized well in advance, it is possible for us to use the results for the purpose of fault detection and performance monitoring between control stations from the stage of system design. particularly in such cases as cascade control systems. In this paper. general properties of the proposed system are analyzed firstly to check whether it is controllable and how its steady responses would be. To simplify calculation, the analysis has been performed based on the transfer equation derived from a modelled case which consists of 2 PI controllers and signal converters between them. including time delay element and first-lag element to consider the situation of signal transmission. The results acquired from simulation are suggested to show how it works actually.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.168-175
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• 2023

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1783-1787
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• 2011

The slip frequency is included in feature frequency for fault diagnosis of rotor bar, so rotating rotor speed is needed. In this study, rotor slot harmonic(RSH) method is suggested for speed estimation of induction motor. When the rotor is rotating, motor current signal include the harmonic signal of back-emf voltage related with number of rotor slot. So from the power spectrum of current signal, the rotor speed can be founded. This method of rotor speed estimation gives the slip frequency, and the feature frequency of rotor bar fault can be calculated. Comparing with stroboscope speed meter, the error rate of suggested method is less than 0.1[%].

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.839-845
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• 2003

This paper introduces multichannel blind source separation (BSS) and multichannel blind deconvolution (MBD) based on higher order statistics of signals from convolutive mixtures. In particular, we are concerned with the case that the number of inputs is the same as the number of outputs. Simulations for two input two output cases are carried out and their performances are assessed. One of the major applications of those sequential algorithms (BSS and MBD) is demonstrated through the fault signal detection from only a single measurement of rotating machine, which offers a certain degree of practicability in the engineering field such as machine health monitoring or condition monitoring.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.367-374
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• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.2
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• pp.136-141
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• 2009

An AC generator is an important components in producing a electric power and so it requires highly reliable protection relays to minimize the possibility of demage occurring under fault conditions. Conventionally, a DFT based RDR has been used for protecting the generator stator winding. However, when DFTs based on Fourier analysis are used, it has been pointed out that defects can occur during the process of transforming a time domain signal into a frequency domain one which can lead to loss of time domain information. This paper proposes the internal fault detection and fault type discrimination for the stator winding by applying the detailed coefficients by Daubechies Wavelet Transform to overcome the defects in the DFT process. For the case studies reported in the paper, a model system was established for the simulations utilizing the ATP, and this verified the effectiveness of the proposed technique through various off-line tests carried out on the collected data. The propose method is shown to be able to rapidly identify internal fault and did not operate a miss-operation for all the external fault tested.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.199-205
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• 2004

This paper describes the fault diagnosis method to order to construct CIM in complex system with hierarchical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement a special neural network. Fault diagnosis system can forecast faults in a system and decide from the signal information of current machine state. Comparing with other diagnosis system for a single fault, the developed system deals with multiple fault diagnosis, comprising hierarchical neural network (HNN). HNN consists of four level neural network, i.e. first is fault symptom classification and second fault diagnosis for item, third is symptom classification and forth fault diagnosis for component. UNIX IPC is used for implementing HNN with multitasking and message transfer between processes in SUN workstation with X-Windows (Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural network represents a separate process in UNIX operating system, information exchanging and cooperating between each neural network was done by message queue.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.12
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• pp.675-683
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• 2003

This paper describes the correction strategy of an overcurrent relay applied in the linked line for interconnecting wind farm with utility power networks in order to improve the capability of a fault detection. The fault current measured in a relaying point might vary according to the fault conditions. Generally, the current of the line to line fault or the line to ground fault in the linked line is much higher than the set value of protective relay due to the large fault level. However, when the high impedance fault occurs in the linked line, we can't detect it by conventional set value because its fault level may be lower than the generating capacity of wind farm. And, the protective relay with conventional set value may generate a trip signal for the insertion of wind turbine generators due to the large transient characteristics. In order to solve above problems and improve protective relaying algorithms applied in the linked line, we propose a new correction strategy of the protective relay in the linked line. The presented method can detect the high impedance fault which can't be detected by conventional relay set value and may prevent the mis-operation of protective relay caused by the insertion of wind farm.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.903-912
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• 2007

Faults of rotating parts of a train normally generate unexpected frequency band or impulsive sound[1] which has a period when it moves with a constant speed. The former can be detected by the moving frame acoustic holography method, which visualizes sound field that is generated by a moving and emitting pure tone or band limited noise source. We have attempted to apply the method to the latter case: the periodic impulsive sound which generate different signal compared with what can be measured by the band limited noise. The signal to noise ratio which determines the success of early fault detection must also be studied with the impulsive and moving signal. This research shows how the problems related with these issues can be resolved. The main idea is that periodic impulsive signal can be expressed by infinite set of discrete pure tones. This enables us to obtain lots of holograms that visualize periodic impulsive sound field including noise by using the moving frame acoustic holography method. Therefore holograms can be averaged to improve the signal to noise ratio until having reliable information that exhibits where the impulsive sources are. Theory and experiment by using the miniature vehicle are described [Work supported by BK21 & KRRI].