• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.54-59
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• 2006

Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have considered the decision tree method as an effective solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for these data.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.851-853
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• 1997

This paper presents an algorithm for real-time restoration of radially operated 154kV loop system. Restoration procedure consists of 4 procedures; recognition of faults, identification of fault locations, seperation of fault locations and restoration of blackout areas. This algorithm adopts expert's knowledge for safe and accurate operation of the real-time restoration system (APRS: Automatic Power Reconfiguration System).

• Journal of the Korean Society for Precision Engineering
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• v.17 no.7
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• pp.98-105
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• 2000

In this study, we developed rotating machine vibration condition monitoring system based upon Windows NT and DSP Board. Developed system includes signal analysis module, trend monitoring and simple diagnosis using threshold value. Trend analysis and report generation are offered with database management tool which was developed in MS-ACCESS environment. Post-processor, based upon Matlab, is developed for vibration signal analysis and fault detection using statistical pattern recognition scheme based upon Bayes discrimination rule and neural networks. Concerning to Bayes discrimination rule, the developed system contains the linear discrimination rule with common covariance matrices and the quadratic discrimination rule under different covariance matrices. Also the system contains k-nearest neighbor method to directly estimate a posterior probability of each class. The result of case studies with the data acquired from Pyung-tak LNG pump and experimental setup show that the system developed in this research is very effective and useful.

• Nuclear Engineering and Technology
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• v.36 no.3
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• pp.219-228
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• 2004

[1] and [2] present an approach to diagnosing possible defects in the mechanical systems of a nuclear power plant. In this paper, by using a fault library as a database and training data, we develop a diagnostic algorithm 1) to decide whether an Air Operated Valve system is sound or not and 2) to identify the defect from which an Air-Operated Valve system suffers, if any. This algorithm is composed of three stages: a neural net stage, a non-neural net stage, and an integration stage. The neural net stage is a simple perceptron, a pattern-recognition module, using a neural net. The non-neural net stage is a simple pattern-matching algorithm, which translates the degree of matching into a corresponding number. The integration stage collects each output and makes a decision. We present a simulation result and confirm that the developed algorithm works accurately, if the input matches one in the database.

• Transactions of the Korean Society of Automotive Engineers
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• v.1 no.2
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• pp.49-59
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• 1993

A new signal processing technique is applied to four-cylinder spark and compression ignition engines for the diagnosis of power faults inside the cylinders. This technique utilizes two-sided directional power spectra(예S) of complex vibration signals measured from engine blocks as the patterns for engine cylinder power faults. The dPSs feature that they give not only the frequency contents but also the directivity of the engine block motion. For the automatic detection/diagnosis of cylinder power faults, pattern recognition method using multi-layer neural networks is employed. Experimental results show that the sucess rate for diagnosis of cylinder power faults using dPSs is higher than that using the conventional one-sided power spectra. The proposed technique is also tested to check the robustness to the sensor position and the engine rotational speed.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.986-989
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• 1987

This paper describes the basic design concepts and architecture of the subscriber line test feature for the TDX-1A digital switching system. Also, implemented software structure for maintaining the multiprocessor control system employed in TDX-1A, which includes terminal server, MML, test, fault recognition and test result handling.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.157.1-157
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• 2001

A new methodology is presented to diagnose faults in equipment plasma. This is accomplished by using neural networks as a pattern recognizer of radio frequency(rf) impedance match data. Using a realtime match monitor system, the match data were collected. The monitor system consisted mainly of a multifunction board and a signal flow diagram coded by Visual Designer. Plasma anomaly was effectively represented by electrical match positions. Twenty sets of fault-symptom patterns were experimentally simulated with experimental variations in process factors, which include rf source power, pressure, Ar and O$_2$ flow rates. As the inputs to neural networks, two means and standard deviations of positions were used ...

• IE interfaces
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• v.20 no.4
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• pp.458-468
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• 2007

Artificial immune systems (AIS) are one of natural computing inspired by the natural immune system. The fault detection, the pattern recognition, the system control and the optimization are major application area of artificial immune systems. This paper gives a concept of artificial immune systems and useful techniques as like the clonal selection, the immune network theory and the negative selection. A concise survey on the optimization problem based on artificial immune systems is generated. The overall performance of artificial immune systems for the optimization problem is discussed.

• Journal of the Korea Safety Management & Science
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• v.14 no.3
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• pp.101-109
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• 2012

It is commonly recognized that railway is one of the representative transportation and it offers public service based on strategies for being rapid, automation, safety. Since the opening of high speed railway, 3-hundred-million people have used it and acknowledged its efficiency. However, derailed accident at Kwangmyeong station in February, 2011, frequent malfunction of KTX-Sancheon, and accidents by engineer's careless fault damaged on credibility of safety, Especially, spreaded accidents through social networking service by cell phones amplified anxiety of public, being criticized by the press. This study analyzed statistics of past accident and cases of passing-by accident, and surveyed 152 KTX captain engineers about their recognition of the accident by careless fault and experiences of possibility of occurrence for preventing engineer's careless fault and restoring trust According to the analysis, engineers worry about responsibility and disadvantages related to the accidents for the most, and they are nervous about malfunction for the second most. This study presents prevention methods regarding the result. First, it is required to improve mental stability and concentration on their work, secondly, advanced ability to cope with malfunction or error through repetitive education and training are required to increase confidence, and for the last, improvement of operational supporting system such as ATP, GPS to prevent errors by human factors. Improvement of the system is expected to lead engineers to prevent careless fault and regain the reputation of railway.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.6
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• pp.55-62
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• 2006

For the fault diagnosis of three-phase induction motors, we construct a experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the data, three-phase current is transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel principal component analysis(KPCA) and linear discriminant analysis(LDA). Finally, we used the classifier based on radial basis function(RBF) network. 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.