• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.1
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• pp.39-55
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• 2011

Process monitoring has been emphasized for the monitoring of complex system such as chemical processing industries to achieve the efficiency enhancement, quality management, safety improvement. Recently, ICA (Independent Component Analysis) based MSPC (Multivariate Statistical Process Control) was widely used in process monitoring approaches. Moreover, DICA (Dynamic ICA) has been introduced to consider the system dynamics. However, the existing approaches show the limitation that their performances are strongly dependent on the statistical distributions of control variables. To improve the limitation, we propose a novel approach for process monitoring by integrating DICA and LOF (Local Outlier Factor). In this paper, we aim to improve the fault detection rate with the proposed method. LOF detects local outliers by using density of surrounding space so that its performance is regardless of data distribution. Therefore, the proposed method not only can consider the system dynamics but can also assure robust performance regardless of the statistical distributions of control variables. Comparison experiments were conducted on the widely used benchmark dataset, Tennessee Eastman process (TE process), and showed the improved performance than existing approaches.

• Applied Biological Chemistry
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• v.50 no.4
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• pp.245-252
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• 2007

FTA (fault tree analysis), an analytical method for system failure management, was employed in the management of faults in running PCR analysis. PCR is executed through several processes, in which the process of PCR machine operation was selected for the analysis by FTA. The reason for choosing the simplest process in the PCR analysis was to adopt it as a first trial to test a feasibility of the FTA approach. First, fault events-top event, intermediate event, basic events-were identified by survey on expert knowledge of PCR. Then those events were correlated deductively to build a fault tree in hierarchical structure. The fault tree was evaluated qualitatively and quantitatively, yielding minimal cut sets, structural importance, common cause vulnerability, simulation of probability of occurrence of top event, cut set importance, item importance and sensitivity. The top event was 'errors in the step of PCR machine operation in running PCR analysis'. The major intermediate events were 'failures in instrument' and 'errors in actions in experiment'. The basic events were four events, one event and one event based on human errors, instrument failure and energy source failure, respectively. Those events were combined with Boolean logic gates-AND or OR, constructing a fault tree. In the qualitative evaluation of the tree, the basic events-'errors in preparing the reaction mixture', 'errors in setting temperature and time of PCR machine', 'failure of electrical power during running PCR machine', 'errors in selecting adequate PCR machine'-proved the most critical in the occurrence of the fault of the top event. In the quantitative evaluation, the list of the critical events were not the same as that from the qualitative evaluation. It was because the probability value of PCR machine failure, not on the list above though, increased with used time, and the probability of the events of electricity failure and defective of PCR machine were given zero due to rare likelihood of the events in general. It was concluded that this feasibility study is worth being a means to introduce the novel technique, FTA, to the management of faults in running PCR analysis.

• Journal of Internet Computing and Services
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• v.1 no.1
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• pp.35-46
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• 2000

One of the main chalenges in personal communication services (PCS) is to locate many mobile terminals that may move frequently from place to place. Such a system operation is called location management. Many network signaling traffic, and database queries are required to achieve such a task. Several strategies have been proposed to improve the efficiency of location management. These strategies use location register databases to store the current location on mobile terminals, and are vulnerable to failure of the location registers. In this paper, we propose a fault-tolerant pointer forwarding with distributed home location register (FT-RFDHLR) to tolerate the failure of location registers. The performance of the proposed method is evaluated by an analytical model, and is compared with thew pointer forwarding with the single home location register (PFSHLR), the pointer forwarding with distributed home location register (PFDHLR), Biaz's bypass forwarding strategy (BFS) and two-path forwarding strategy (TPFS).

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.25-38
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• 2017

Purpose: Hot strip rolling mill consists of a lot of mechanical and electrical units. In condition monitoring and diagnosis phase, various units could be failed with unknown reasons. In this study, we propose an effective method to detect early the units with abnormal status to minimize system downtime. Methods: The early warning problem with various units is defined. K-means and PAM algorithm with Euclidean and Manhattan distances were performed to detect the abnormal status. In addition, an performance of the proposed algorithm is investigated by field data analysis. Results: PAM with Manhattan distance(PAM_ManD) showed better results than K-means algorithm with Euclidean distance(K-means_ED). In addition, we could know from multivariate field data analysis that the system reliability of hot strip rolling mill can be increased by detecting early abnormal status. Conclusion: In this paper, clustering-based monitoring and fault detection algorithm using Manhattan distance is proposed. Experiments are performed to study the benefit of the PAM with Manhattan distance against the K-means with Euclidean distance.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.11 no.2
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• pp.92-96
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• 2006

NLPCA(Nonlinear Principal Component Analysis) is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA operates by a feedforward neural network called AANN(Auto Associative Neural Network) which performs the identity mapping. In this work, a sensor fault detection system based on NLPCA is presented. To verify its applicability, simulation study on the data supplied from risk management system is executed.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.513-521
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• 2017

HVDC protection setting is difficult to be calculated analytically because of its strong nonlinearity. The currently used setting method is based on the empirical setting of previous projects and then verified by digital simulation. It entails a huge workload and low efficiency. To facilitate protection setting, this paper systematically summarizes the HVDC protection characteristics and then presents a classification of HVDC protections according to the protection principles. On the basis of the fault quantities, a novel setting procedure suitable for travelling wave protection, derivative and level protection, and differential protection is proposed. The proposed procedure is illustrated and verified in detail with the example of travelling wave protection. An HVDC protection setting system that has the functions of automatic protection setting and data management is developed utilizing the C# programming language.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.2
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• pp.199-204
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• 2013

Recently, the demand for fuel efficient electric vehicles (EVs) and hybrid electric vehicles (HEVs) has been growing globally. Due to the increased number of switching devices in the electrified vehicles, the probability of the semiconductor device failure is much higher than in other application areas. A sudden failure in one of the power switches and insufficient power management ability in the systems not only decreases system performance, but also leads to critical safety problems. In this paper, novel switch open circuit fault detection method is proposed, and the proposed approach is verified by experiments.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.59-62
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• 2009

When happen the electrical facilities accident the one's diagnosis system of fault cause was constructed by FMEA method Cable accident cause is given by accident cause that can happen in each one's diagnosis and accident probability value. From the verification of system, the one's diagnosis system agreed well with result that analyzed actual state. Thus, the system is judged to be used effectively examine for accident cause of electrical facilities.

• Journal of the Korea Safety Management & Science
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• v.1 no.1
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• pp.7-17
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• 1999

As the loss from break-downs and errors, which became more frequent with the growth of elaborateness, complexity and in scale of the plant and equipments, are enormous, the improvement in the reliability, maintenance, safety, and qualify become to have interest. The fault diagnosis is a systematic and unified method to find errors, which is based on the interpretation that data, subconsciously, have noises. But, as most of the methods are inferences based on binomial logic, the uncertainty is not correctly reflected. In this study, we suggest, to manage the uncertainty in the system efficiently on the point of predictive maintenance, We should use fuzzy expert system, which make the decision considering uncertainty possible by taking linguistical variable and fixed quantity by using the fuzzy theory concepts on the basis of an expert's direct observation and experience.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.242-250
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• 2019

In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.