• 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.

• Journal of Wind Energy
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• v.13 no.3
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• pp.33-42
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• 2022

In this paper, a semi-supervised machine learning technique applied to actual field vibration data acquired from Jeju-do wind turbines for predictive diagnosis of abnormal conditions of offshore wind turbines is introduced. Semi-supervised machine learning, which combines un-supervised learning with supervised learning, can be used to perform anomaly detection in situations where sufficient fault data cannot be obtained. The signal processing results using the spectrogram of the original signal were shown, and external data were used to overcome the problem that disturbance reactions easily occurred due to the imbalance between the number of normal and abnormal data. Out of distribution (OOD), which uses external data, is a technology that is regarded as abnormal data that is unlikely to occur in reality, but we were able to use it by expanding it. By rearranging the distribution of data in this way, classification can be performed more robustly. Specifically, by observing the trends of the abnormal score and the change in the feature of the representation layer, continuous learning was performed through a mixture of existing and new data.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.413-413
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• 2010

In semiconductor manufacturing field, all equipments have various sensors to diagnosis the situations of processes. For increasing the accuracy of diagnosis, hundreds of sensors are emplyed. As sensors provide millions of data, the process diagnosis from them are unrealistic. Besides, in some cases, the results from some data which have same conditions are different. We want to find some information, such as data and knowledge, from the data. Nowadays, fault detection and classification (FDC) has been concerned to increasing the yield. Certain faults and no-faults can be classified by various FDC tools. The uncertainty in semiconductor manufacturing, no-faulty in faulty and faulty in no-faulty, has been caused the productivity to decreased. From the uncertainty, the rough set theory is a viable approach for extraction of meaningful knowledge and making predictions. Reduction of data sets, finding hidden data patterns, and generation of decision rules contrasts other approaches such as regression analysis and neural networks. In this research, a RGB sensor was used for diagnosis plasma instead of optical emission spectroscopy (OES). RGB data has just three variables (red, green and blue), while OES data has thousands of variables. RGB data, however, is difficult to analyze by human's eyes. Same outputs in a variable show different outcomes. In other words, RGB data includes the uncertainty. In this research, by rough set theory, decision rules were generated. In decision rules, we could find the hidden data patterns from the uncertainty. RGB sensor can diagnosis the change of plasma condition as over 90% accuracy by the rough set theory. Although we only present a preliminary research result, in this paper, we will continuously develop uncertainty problem solving data mining algorithm for the application of semiconductor process diagnosis.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_2
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• pp.1117-1125
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• 2023

The DC part of the DC microgrid power conversion system uses capacitors for buffers of charge and discharge energy for smoothing voltage and plays important roles such as high frequency component absorption, power balancing, and voltage ripple reduction. The capacitor uses an aluminum electrolytic capacitor, which has advantages of capacity, low price, and relatively fast charging/discharging characteristics. Aluminum electrolytic capacitors(AEC) have previous advantages, but over time, the capacity of the capacitors decreases due to deterioration and an increase in internal temperature, resulting in a decrease in use efficiency or an accident such as steam extraction due to electrolyte evaporation. It is necessary to take measures to prevent accidents because the failure diagnosis and detection of such capacitors are a very important part of the long-term operation, safety of use, and reliability of the power conversion system because the failure of the capacitor leads to not only a single problem but also a short circuit accident of the power conversion system.

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.1998-2008
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• 2020

Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV.

• Journal of the Korean Institute of Gas
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• v.18 no.3
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• pp.53-59
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• 2014

In this study, we suggest a system to build the monitoring model for compressed natural gas (CNG) stations, operated in only non-stationary modes, and perform the real-time monitoring and the abnormality diagnosis using principal component analysis (PCA) that is suitable for processing large amounts of multi-dimensional data among multivariate statistical analysis methods. We build the model by the calculation of the new characteristic variables, called as the major components, finding the factors representing the trend of process operation, or a combination of variables among 7 pressure sensor data and 5 temperature sensor data collected from a CNG station at every second. The real-time monitoring is performed reflecting the data of process operation measured in real-time against the built model. As a result of conducting the test of monitoring in order to improve the accuracy of the system and verification, all data in the normal operation were distinguished as normal. The cause of abnormality could be refined, when abnormality was detected successfully, by tracking the variables out of the score plot.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.2
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• pp.121-125
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• 2010

The infrared thermography technology rather than traditional nondestructive methods has benefits with non-contact and non-destructive testings in measuring for the fault diagnosis of the rotating machine. In this work, condition monitoring measurements using this advantage of thermography were proposed. From this study, the novel approach for the damage detection of a rotating machine was conducted based on the spectrum analysis. As results, by adopting the ball bearing used in the rotating machine applied extensively, an spectrum analysis with thermal imaging experiment was performed. Also, as analysing the temperature characteristics obtained from the infrared thermography for in-situ rotating ball bearing under the lubrication condition, it was concluded that infrared thermography for condition monitoring in the rotating machine at real time could be utilized in many industrial fields.

• Journal of Convergence for Information Technology
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• v.9 no.10
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• pp.101-107
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• 2019

This paper proposes a method for detecting faults in conveyor systems used for transportation of raw materials needed in the thermal power plant and cement industries. A small drone was designed in consideration of the difficulty in accessing the industrial site and the need to use it in wide industrial site. In order to apply the system to the embedded microprocessor, hardware and algorithms considering limited memory and execution time have been proposed. At this time, the failure determination method measures the peak frequency through the measurement, detects the continuity of the high frequency, and performs the failure diagnosis with the high frequency components of noise. The proposed system consists of experimental environment based on the data obtained from the actual thermal power plant, and it is confirmed that the proposed system is useful by conducting virtual environment experiments with the drone designed system. In the future, further research is needed to improve the drone's flight stability and to improve discrimination performance by using more intelligent methods of fault frequency.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.35-40
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• 2017

Out-of-limit (OOL) alarm method that is threshold checking of telemetry value is widely used for the satellites fault diagnosis and health monitoring. However, it requires engineering knowledge and effort to define delicate threshold value and has limitations that anomalous behaviors within the defined limits can't be detected. In this paper, we propose a satellite anomalous behavior detection system through fuzzy model that is composed by important statistical feature selected by rough-set theory. Not pre-defined anomaly is detected because only normal state data is used for fuzzy model. Also, anomalous behavior within the threshold limit is detected by using statistic feature that can be collected without engineering knowledge. The proposed system successfully detected non-ordinary state for battery temperature telemetry.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.3
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• pp.254-260
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• 2002

In this study, two types of mobile robotic systems using NDT (Non-destructive testing) method are developed for automatic diagnosis of the boiler tubes and large size pipelines. The developed mobile robots crawl the outer surface of the tubes or pipelines and detect in-pipe defects such as pinholes, cracks and thickness reduction by corrosion and/or erosion using EMAT (Electro-magnetic Acoustic Transducer). Automation of fault detection by means of mobile robotic systems for these large-scale structures helps to prevent significant troubles without danger of human beings under harmful environment.