• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.8
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• pp.965-970
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• 2011

We develop a diagnostic system to monitor failures in an engine-assembly line. Existing techniques such as sensory analysis, time domain analysis, frequency analysis, and statistical analysis have limitations in the diagnosis of engine-assembly failure when there are abnormal vibration waveforms (crashing and damping signals) during the assembly. We use a wavelet technique to deal with crashing and damping signals. We also implement a new technique for developing diagnostic rules from sensor data, and we demonstrate its validity.

• Journal of Power Electronics
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• v.11 no.4
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• pp.533-541
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• 2011

The redundant operation of a parallel AC to DC converter via a serial communication bus is presented. The proposed system consists of three isolated CUK power factor correction modules. The controller for each converter is a dsPIC30F6010 microcontroller while a RS485 communication bus and the clock signal are used for synchronizing the data communication. The control strategy of the redundant operation relies on the communication of information among each of the modules, which communicate via a RS485 serial bus. This information is received from the communication checks of the converter module connected to the system to share the load current. Performance evaluations were conducted through experimentation on a three-module parallel-connected prototype, with a 578W load and a -48V dc output voltage. The proposed system has achieved the following: the current sharing is quite good, both the transient response and the steady state. The converter modules can perform the current sharing immediately, when a fault is found in another converter module. In addition, the transient response occurs in the system, and the output voltages are at their minimum overshoot and undershoot. Finally, the proposed system has a relatively simple implementation for the redundant operation.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.4
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• pp.22-35
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• 1992

In highly automated machining centers, intelligent sensor fddeback systems are indispensable on order to monitor their operations, to ensure efficient metal removal, and to initate remedial action in the event of accident. In this study, an on-line tool wear detection system for thrning operations is developed, and experimentally evaluated. The system employs multiple sensors and the signals from these sensors are processed using a multichannel autoegressive (AR) series model. The resulting output from the signal processing block is then fed to a previously tranied artificial neural network (multiayered perceptron) to make a final decision on the state of the cutting tool. To learn the necessary input/output mapping for tool wear detection, the weithts and thresholds of the network are adjusted according to the back propagation (BP) method during off-line training. The results of experimental evaluation show that the system works well over a wide range of cutting conditions, and the ability of the system to detect tool wear is improved due to the generalization, fault-tolearant and self-ofganizing properties of the neural network.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.4
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• pp.109-115
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• 2005

In the study, Methods for operating measures in equipment security to find out dangerousness timely in the system and to need for the prevention and measures. The method for analyzing and reconstructing the causes of accident of equipment in site, and try to save the information of site in real-time and to analyze the state of equipment to look for the factors of accidents. By this analysis, one plan for efficiency of production, Equipment Fault Diagnosis Management and security is integrating and building module of using the Fuzzy Inference based on fuzzy theory. The case study is applied to the industrial electric motors that are necessarily used to all manufacturing equipment. Using the sensor for temperature is attached to gain the site information in real time and to design the hardware module for signal processing. In software, realize the system supervising and automatically saving to management data base by the algorithm based in fuzzy theory from the existing manual input system

• The Journal of the Acoustical Society of Korea
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• v.14 no.2E
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• pp.43-49
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• 1995

In this paper a space search technique is used to detect and locate the faults of the circuit interconnections. The circuit interconnections are represented by the tree structure and the tree space is searched to detect and locate the open faults of the circuit interconnections. The breadth search is used to detect the open faults and reduce the space size. The depth search is used to locate the open faults.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.8
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• pp.55-61
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• 2010

In this paper, we proposed selective extraction method of frequency information and PCA-ELM based diagnosis system for three-phase induction motors. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by variance As the next step, feature extraction is performed by principal component analysis (PCA). Finally, we used the classifier based on Extreme Learning Machine (ELM) with fast learning procedure. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.1
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• pp.23-28
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• 2015

Measuring the distances between signals in the signal space is usually determined by obtaining the ideal metric which is not easy to obtain. In this research we have investigated the scheme that measures the distances between the signals constructed with the measured voltage signals connected to electric apparatus using Kalman filter and exponential mapping. The metric is defined on the feature signals obtained via the estimation process of a Kalman filter and the mapping process using the exponential transformation. Diagnosis is on the voltage fluctuations is applied to determining whether the system is in the stable state or not due to the unexpected accidents, such as power overcharge, discharge, outages flow may be the cause of the accident. The decision making scheme evaluated with respect to the effectiveness and the degree of complication with different variances. Two methods, the Hard Limit Threshold Scheme(HLTS) and the Interval Energy Scheme(IES) are proposed and compared. In experiments the IES shows better tolerance to impulse noise than the HLTS.

• 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.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.342-344
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• 2003

This paper describes the correction of overcurrent protective relaying set value in distribution networks interconnected with wind farm by dedicated line. The wind farm composed of wind turbine generators is one of the great energy sources; however, it would be also highly possible that the current in the point of common coupling is influenced by the output power of wind farm. So, the overcurrent relay applied in distribution feeders might generate trip signal for normal operation. In order to prevent the mal-operation of overcurrent relay, it is necessary to correct the relay's setting value according to the output power of end farm. This paper presents the influence of wind farm on the overcurrent relaying set value in distribution feeders for cases of fault as well as normal operation and proposes the basic strategy for correction of overcurrent relaying set value.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.427-430
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• 2002

Recently the subject of "wavelet analysis" has be drawn by both mathematical and engineering application fields such as Signal Processing, Compression/Decomposition, Wavelet-Neural Network, Statistics and etc. Even though its similar to Fourier analysis, wavelet is a versatile tool with much mathematical content and great potential for applications. Especially, wavelet transform uses localizable various mother wavelet functions in time-frequency domain. Therefore, wavelet transform has good time-analysis ability for high frequency component, and has good frequency-analysis ability for low frequency component. Using the discriminative ability is more easy method than other conventional techniques. In this paper, Morlet wavelet transform was applied to discriminate the kind of line fault by acquired data from real power transformation network. The experimental result presented that Morlet wavelet transform is easier,and more useful method than the FFT (Fast Fourier Transform).