• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.691-697
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• 2006

Induction motors are critical components in industrial process. So there are many research in the condition based maintenance, online monitoring system, and fault detection. This paper presents a scheme on the detection and diagnosis of the three-phase squirrel induction motor under unbalanced voltage, broken rotor bar, and a combination of these two faults. Actually one fault happen in operation, it influence other component in motor or cause another faults. Accordingly it is useful to diagnose and detect a combination fault in induction motor as well as each fault. The proposed fault detection and diagnosis algorithm is based on the stator currents from the squirrel induction motor and simulated with the aid of Matlab Simulink.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.161-163
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• 1987

An FTCS is developed for the purpose of improving the reliability of a process control system. The proposed FTCS has capabilities of failure detection, back-up control, graphic display, and self-checking. Also the FTCS is combined with the process simulator to experiment in laboratory for the evaluation of performance of operation. The FTCS is applied to Thermal Power Plant .

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.372-375
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• 1989

In this paper, a new FTCS with the ability to perform original control objective without considerable loss of control performance in the face of any fault is proposed. The FTCS is composed of two interacting units, Adaptive Controller Unit and Fault Detection/Classification, where ACU performs primary control objective with basic process information(I/O) and environmental information fed by FDU and where FDU detect and classify faults and make decision on remidial action by the use of information provided by ACU.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.15-19
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• 1994

The application of neural network for fault dection of cutting force in turning was introduced. This monitoring system consist of a RBF predicton model and a ART-1 pattern classifier. RBF prediction model predict a cutting force signal. Prediction error of predictor is used for a input vector of ART-1 pattern classifier. Prediction error could be successfully performed to fault signal monitoring of ART-1 pattern classifier.

• Journal of the Korea Computer Industry Society
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• v.3 no.7
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• pp.837-844
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• 2002

In general, software test is carried out to detect or repair errors in system during software development process. Namely, we can evaluate software reliability through collecting and removing the faults detected in testing phase. Software reliability growth model evaluates reliability of software mathematically. Many kinds of software reliability growth modeling which modeling the processes of detecting, revising and removing the faults detected in testing phase have been proposed in many ways. and, it is assumed that almost of these modeling have one typed detect and show the uniformed detection rate. In this study, significance levels of the faults detected in test phase are classified according to how they can affect on the whole system and then the fault detection capability of them is applied. From this point of view, We here by propose a software reliability growth model with faults detection capability according considering fault significance levels and apply some fault data to this proposed model and finally verify its validity by comparing and estimating with the existing modeling.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.32-38
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• 2007

A batch Process has a multi-way data structure that consists of batch-time-variable axis, so the statistical modeling of a batch process is a difficult and challenging issue to the process engineers. In this study, We applied a statistical process control technique to the general batch process data, and implemented a fault-detection and Statistical process control system that was able to detect, identify and diagnose the fault. Semiconductor etch process and semi-batch styrene-butadiene rubber process data are used to case study. Before the modeling, we pre-processed the data using the multi-way unfolding technique to decompose the data structure. Multivariate regression techniques like support vector regression and partial least squares were used to identify the relation between the process variables and process condition. Finally, we constructed the root mean squared error chart and variable contribution chart to diagnose the faults.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.10
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• pp.56-62
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• 2014

When fault currents contain decaying DC offset, the peak value of the fault current in the first cycle of the fault period is higher than the fault current during the steady-state period. To reduce the asymmetric fault current, this paper proposes an operation scheme using the series connection of two hybrid type Superconducting Fault Current Limiters (SFCLs) : an auxiliary SFCL and a main SFCL. The proposed method calculates the fault angle by comparing the zero-crossing time with fault detection time. According to the fault angle calculated, an auxiliary SFCL operates to reduce an asymmetric fault current during half a cycle after fault occurrence. After this process, the fault current is limited by a main SFCL. To confirm the usefulness of the proposed method, case studies using Electro-Magnetic Transients Program (EMTP)/Alternative Transient Program (ATP) Draw are perfomed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1442-1449
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• 2016

This paper proposes a diagnosis to detecting poor contact fault and fault location. Electrical fire by poor contact fault of power cable occupied a large proportion in the total electrical installations. The proposed method has an object to prevent electrical fault in advance. But detecting poor contact fault is difficult to detect fault type and fault location by using conventional reflectometry due to faults generated intermittently and repeatedly on the time change. Therefore, in this paper poor contact fault and fault conditions were defined. System generating poor contact fault produced for the experimental setup. SSTDR and algorithm of reference signal elimination heighten performance detecting poor contact fault on live power cable. The diagnosis methods of signal process and analysis of reflected signal was proposed for detecting poor contact fault and fault location. The poor contact fault and location had been detected through proposed diagnosis methods. The fault location and error rate of detection were verified detecting accuracy by experiment results.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.36-44
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• 2010

This paper presents the design of an algorithm to detect, identify, and locate faults in radial distribution feeders of Provincial Electricity Authority (PEA). The algorithm consists of three major steps. First, the adaptive algorithm is applied to track/estimate the system electrical parameter, i.e. current phasor, voltage phasor, and impedance. Next process, the impedance rule base is used to detect and identify the type of fault. Finally, the current compensation technique and a geographic information system (GIS) are applied to evaluate a possible fault location. The paper also shows the results from field tests of the automate fault location and illustrates the effectiveness of the proposed fault location scheme.