• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.8
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• pp.98-103
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• 2015

Power plant is that very high reliability when industrial and economic impact on the overall electric power system is required, it is essential to improve the reliability, especially the fault prediction diagnosis. Since an accident caused by the partial discharge in the power plant is above state has a faster response characteristic than the other indications in the case of any, the partial discharge generated in the power plant immediately detect the deterioration of insulation due to the accident of the power plant and the non-drawn It should prevent or reduce. Partial Discharge Measuring Systems for UHV SF6 Gas Insulated Switchgear and power transformer on site installed has some probability of abnormal recognition in case of non-flexible deal with on site noise. Many methode to eliminate these kinds of noises, UHF Detection System is chosen as purchase description in Korea, but this system having a bandwidth between 500MHz 1.5GHz wide band. Initial install periods(about 20 years ago), this band had no strong signal source, but in these days this wide band have strong signals, such as LTE. So, module described in this paper is designed as simultaneously use with wide and narrow band for solve this noise problem, and introduce this system.

• Journal of Aerospace System Engineering
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• v.13 no.2
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• pp.26-33
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• 2019

Control moment gyro (CMG) employed as satellite actuators, generates a large torque through the steering of its gimbals. Although each gimbal holds a high-speed rotating wheel, the wheel imbalances induces disturbance and degrades the satellite control quality. Therefore, the disturbances ought to be detected and identified as a precaution against actuator faults. Among the method used in detecting disturbances is the state observers. In this paper, we apply a continuous second order sliding mode observer to detect single disturbances/faults in CMGs. Verification of the algorithm is also done on the hardware satellite simulator where four CMGs are installed.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.127-134
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• 2021

In this study, an anomaly detection (AD) algorithm was implemented to detect the failure of a marine air compressor. A lab-scale experiment was designed to produce fault datasets (time-series electric current measurements) for 10 failure modes of the air compressor. The results demonstrated that the temporal pattern of the datasets showed periodicity with a different period, depending on the failure mode. An AD model with a convolutional autoencoder was developed and trained based on a normal operation dataset. The reconstruction error was used as the threshold for AD. The reconstruction error was noted to be dependent on the AD model and hyperparameter tuning. The AD model was applied to the synthetic dataset, which comprised both normal and abnormal conditions of the air compressor for validation. The AD model exhibited good detection performance on anomalies showing periodicity but poor performance on anomalies resulting from subtle load changes in the motor.

• Design & Manufacturing
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• v.16 no.4
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1516-1529
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• 2023

In this paper, a motor gearbox fault-detection system based on a hierarchical flow-based model is proposed. The proposed system is used for the anomaly detection of a motion sound-based actuator module. The proposed flow-based model, which is a generative model, learns by directly modeling a data distribution function. As the objective function is the maximum likelihood value of the input data, the training is stable and simple to use for anomaly detection. The operation sound of a car's side-view mirror motor is converted into a Mel-spectrogram image, consisting of a folding signal and an unfolding signal, and used as training data in this experiment. The proposed system is composed of an encoder and a decoder. The data extracted from the layer of the pretrained feature extractor are used as the decoder input data in the encoder. This information is used in the decoder by performing an interlayer cross-scale convolution operation. The experimental results indicate that the context information of various dimensions extracted from the interlayer hierarchical data improves the defect detection accuracy. This paper is notable because it uses acoustic data and a normalizing flow model to detect outliers based on the features of experimental data.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.113-118
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• 2023

Maintenance of semiconductor equipment processes is crucial for the continuous growth of the semiconductor market. The process must always be upheld in optimal condition to ensure a smooth supply of numerous parts. Additionally, it is imperative to monitor the status of the robots that play a central role in the process. Just as many senses of organs judge a person's body condition, robots also have numerous sensors that play a role, and like human joints, they can detect the condition first in the joints, which are the driving parts of the robot. Therefore, a normal state test bed and an abnormal state test bed using an aging reducer were constructed by simulating the joint, which is the driving part of the robot. Various sensors such as vibration, torque, encoder, and temperature were attached to accurately diagnose the robot's failure, and the test bed was built with an integrated system to collect and control data simultaneously in real-time. After configuring the user screen and building a database based on the collected data, the characteristic values of normal and abnormal data were analyzed, and machine learning was performed using the KNN (K-Nearest Neighbors) machine learning algorithm. This approach yielded an impressive 94% accuracy in failure diagnosis, underscoring the reliability of both the test bed and the data it produced.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.30-38
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• 2006

In this paper, we improved to drops recognition rate according to distance using distance and depth information with 3D from stereo face images. A monocular face image has problem to drops recognition rate by uncertainty information such as distance of an object, size, moving, rotation, and depth. Also, if image information was not acquired such as rotation, illumination, and pose change for recognition, it has a very many fault. So, we wish to solve such problem. Proposed method consists of an eyes detection algorithm, analysis a pose of face, md principal component analysis (PCA). We also convert the YCbCr space from the RGB for detect with fast face in a limited region. We create multi-layered relative intensity map in face candidate region and decide whether it is face from facial geometry. It can acquire the depth information of distance, eyes, and mouth in stereo face images. Proposed method detects face according to scale, moving, and rotation by using distance and depth. We train by using PCA the detected left face and estimated direction difference. Simulation results with face recognition rate of 95.83% (100cm) in the front and 98.3% with the pose change were obtained successfully. Therefore, proposed method can be used to obtain high recognition rate with an appropriate scaling and pose change according to the distance.

• Journal of Sensor Science and Technology
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• v.9 no.6
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• pp.404-415
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• 2000

This paper describes several performances and nondestructive inspection for deterioration due to forest fires in overhead transmission lines. After discussing corrosion mechanism such as atmospheric and galvanic corrosion for aged ACSR conductors and its detection for them are presented. Through impedance analysis of a solenoid coil, it is shown that the eddy current sensor may be available to inspect severe fault or local corrosion. As the solenoid coil changes its impedance when the test conductor is inserted into the coil, it can be possible to measure deterioration degree caused by forest fires. Tensile strength, extension rate and sensor impedance are tested for some samples degraded by artificial fire. As increasing blazed period to some extent, the strength of aluminum strand begins to be reduced remarkably, while galvanized steel strand holds the similar strength to the initial value, despite of appearing a little loss of zinc layer. In general, it is shown that the sensor impedance would be increased while the tension load of conductor is reduced and the extension rate is contrarily increased. Therefore, the sensor output could exhibit the changes of mechanical performances, and would be used to detect such deterioration caused by forest fire in ACSR conductors built on the ridge of mountains. Finally, it was verified that the solenoid coil could be applicable to obtain any crucial inform for serious deterioration due to forest fires.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.175-180
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• 2015

With progress in industrialization, facilities for generating, delivering, and receiving high levels of electric power are in great demand. The scale of electric power equipment is increasing in both size and complexity. This has contributed to the development of our modern, high-tech and information-based society. However, if the generation of electric power is suspended due to unexpected accidents at power facilities or power stations, a range of equipment the operations of which are dependent on electric power can be damaged, causing substantial socioeconomic losses in an industrial society. A great deal of time and money would be expended to repair damaged facilities at a power station, causing enormous economic loss.In order to detect the deterioration processes of power cables, and to prevent the destruction of power cables, the operation status of power cables should be monitored on a regular basis. We have installed equipment at Korea Western Power Co., Ltd., located in Taean, in order to predict and prevent the destruction of power cables. This is an entirely new installation: a set of equipment invented specifically to measure the insulation resistance of power cables. Installation of the equipment does not cause the flow of earth fault current. This ensures accurate measurement of insulation resistance values by the equipment. We have been studying this equipment in order to develop preventive technology that would show the deterioration processes of power cables.

• Journal of the Korean Institute of Gas
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• v.20 no.6
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• pp.37-42
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• 2016

The purpose of this paper is to study for the emission gas control of passenger car. The first example, the PCSV never open when operating condition, but it opened by causing malfunction because of trouble. As a result, the purge gas entered into surge tank, a mount of fuel was displayed with excessive supply on tester. Therefore, it certified the bad-condition of the engine when idling by decreasing of fuel injection quantity from engine ECU. The second example, the hose activating a EGR valve didn't supply the vacuum pressure because of assembling the other part. Thus, it knew the bad-condition of engine that the EGR valve would not work normally by leaking with the other port. The third example, as the rear oxygen sensor of two sensor were fault-installing by changing the sensor of other a car it could not detect of oxygen quantity. Finally, it found the phenomenon of abruptly decreasing vehicle speed when braking a car. Therefore, the system including with emission control has to drastically manage by maximizing condition to role decreasing the emission gas.