• 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.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1039-1041
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• 1998

This paper presents a practical fuzzy expert system to diagnose various faults occurred in local power systems. This integrated system can diagnose all faults occurred in a transmission network and substations. In this paper. the fuzzy reasoning of the diagnostic process is discussed in detail. The discrimination of false operations and non-operations of protective devices as well as the fault identification scheme are also analyzed together with the fuzzy inference process.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.1
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• pp.28-36
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• 2007

Sensor networks are usually composed of tens or thousands of tiny devices with limited resources. Because of their limited resources, many researchers have studied on the energy management in the WSNs(Wireless Sensor Networks), especially taking into account communications efficiency. For effective data transmission and sensor fault detection in sensor network environment, a new remote monitoring system based on PCA(Principle Component Analysis) and AANN(Auto Associative Neural Network) is proposed. PCA and AANN have emerged as a useful tool for data compression and identification of abnormal data. Proposed system can be effectively applied to sensor network working in LEA2C(Low Energy Adaptive Connectionist Clustering) routing algorithms. To verify its applicability, some simulation studies on the data obtained from real WSNs are executed.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.152-156
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• 1996

Identification of incipient faults and various events on the distribution feeders is very important to develop the prediction method of fault symptom. In this paper, the configuration of data acquisition system to get the real field data is introduced. And the Quantification of incipient faults is also discussed. Based on the acquired field data, how the time domain parameters of voltage and current signals are applied to this research is partly introduced.

• Journal of Drive and Control
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• v.20 no.4
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• pp.54-63
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• 2023

High and intermittent loading cycles induce fatigue damage to transmission components, resulting in premature gearbox failure. To identify gearbox defects, numerous vibration-based diagnostics techniques, using several artificial intelligence (AI) algorithms, have recently been presented. In this paper, an optimized deep belief network (DBN) model for gearbox problem diagnosis was designed based on time-frequency visual pattern identification. To optimize the hyperparameters of the model, a particle swarm optimization (PSO) approach was integrated into the DBN. The proposed model was tested on two gearbox datasets: a wind turbine gearbox and an experimental gearbox. The optimized DBN model demonstrated strong and robust performance in classification accuracy. In addition, the accuracy of the generated datasets was compared using traditional ML and DL algorithms. Furthermore, the proposed model was evaluated on different partitions of the dataset. The results showed that, even with a small amount of sample data, the optimized DBN model achieved high accuracy in diagnosis.

• Journal of Advanced Research in Ocean Engineering
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• v.3 no.3
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• pp.125-135
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• 2017

The paper considers the practical application of the FMEA(Failure Mode and Effect Analysis) method to assess the operational reliability of the LNG(Liquefied Natural Gas) transfer system, which is a potential problem for the connection between the LNG FPSO and LNG carrier. Hazard Identification (HAZID) and Hazard operability (HAZOP) are applied to identify the risks and hazards during the operation of LNG transfer system. The approach is performed for the FMEA to assess the reliability based on the detection of defects typical to LNG transfer system. FTA and FMEA associated with a probabilistic risk database to the operation scenarios are applied to assess the risk. After providing an outline of the safety assessment procedure for the operational problems of system, safety assessment example is presented, providing details on the fault tree of operational accident, safety assessment, and risk measures.

• Journal of Electrical Engineering and Technology
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• v.3 no.3
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• pp.331-336
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• 2008

Electric load components have different characteristics according to the variation of voltage and frequency. This paper presents the load modeling of an electric locomotive by the parameter identification method. The proposed method for load modeling is very simple and easy for application. The proposed load model of the electric locomotive is represented by the combination of the loads that have static and dynamic characteristics. This load modeling is applied to the KTX in Korea to verify the effectiveness of the proposed method. The results of proposed load modeling by the parameter identification follow the field measurements very exactly.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.08a
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• pp.102-112
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• 2004

In this paper, we propose a human model for analysis for human work pattern or human fault, where a gantry crane simulator is used to survey the property of human operation. From the input and output of gantry crane response, we make a human operation model by using conventional ARX identification method. For identify the human model, we assume the eight inputs and two outputs. By using the input/output data, we estimate the parameters of ARX of the human system model. For verify the proposed method, we compared the real data with the modeled data, where three kinds of work trajectory path are used.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.4
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• pp.42-49
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• 1998

This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surface from which the particles originated. The morphological identification of wear debris can therefore provide very early detection of a fault and can also often facilitate a diagnosis. The purpose of this study is to attempt the developement of intelligent system for moving condition diagnosis of the machine driving system. The four shape parameter(50% volumetric diameter, aspect, roundness and reflectivity) of war debris are used as inputs to the neural network and learned the moving condition of five values(material3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristics and recognized the moving condition and materials very well by neural network.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.11
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• pp.764-770
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• 2015

A crack identification method using an equivalent bending stiffness and natural frequency for cracked beam is presented. Modal properties of cantilever beam with a tip mass is identified by applying the boundary conditions to a general solution. An equivalent bending stiffness for cracked beam based on an energy method is used to identify natural frequencies of cantilever thin-walled pipe with a tip mass, which has a through-the-thickness crack, subjected to bending. The identified natural frequencies of the cracked beam are used in constructing training patterns of neural networks. Then crack location and size are identified using a committee of the neural networks. Crack detection was carried out for an example beam using the proposed method, and the identified crack locations and sizes agree reasonably well with the exact values.