• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.96-101
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• 2014

Wind power is the fastest growing renewable energy source in the world and it is expected to remain so for some times. Recently, there is a constant need for the reduction of Operational and Maintenance(O&M) costs of Wind Energy Conversion Systems(WECS). The most efficient way of reducing O&M cost would be to utilize CMS(Condition Monitoring System) of WECS. CMS allows for early detection of the deterioration of the wind generator's health, facilitating a proactive action, minimizing downtime, and finally maximizing productivity. There are two types of faults such as mass unbalance and aerodynamic asymmetry which are related to wind turbine's rotor faults. Generally, these faults tend to generate various vibrations. Therefore, in this work a simple fault detection algorithm based on spectrums of vibration signals and simple max-min decision logic is proposed. Furthermore, in order to verify its feasibility, several simulation studies are carried out by using GH-bladed software.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.275-281
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• 2013

This space is for the of your study in English. If simple menu item changes or the addition of check items are necessary on GUI menu of existing test equipments for military facilities that are programmed by using RAD tools such as Visual C++, they should go through complex steps, such as numerous conducting steps, coding, flash design modification, recompiling and distribution. It is cumbersome process and waste much time. Also, on implementing them, it was worried about leaking secrets because a number of military security considerations were included. To solve such as the above problem, we proposed commercial RIA technologies and a COTS fault diagnostic knowledge-based system that implemented by the XML data design technique in this research. The proposed approach solves the problem of existing methods, reduced inspection time, and improved performance, usability, and maintainability.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.213-217
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). For teaming the NN, a BPN with one hidden, one input and one output layer was used. The input layer had seven neurons of variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer used 6 neurons of degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network teaming and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.6
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• pp.797-804
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• 2013

In this study, the authors develop a methodology for a diagnostic system with a vibration parameter that is influenced by environmental factors. The data tends to have a varying average over time. Often, these features are found in statistical data retrieved from a production line. If we utilize existing statistical techniques for these features, we could derive an incorrect diagnostic conclusion based on the different average values. To overcome the limitations of previous methods, the authors apply a function analyzed through regression analysis to predict the mean value and corresponding upper and lower limits at each stage. This technique also provides corresponding statistical parameters in varying dynamic means. To validate the proposed methods, we retrieve data from the engine assembly line of H Motors and verify the results.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.15-22
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV(Unmanned Aerial Vehicle) which is being developed by KARI (Korea Aerospace Research Institute). For teeming the NN(Neural Network), a BPN(Back Propagation Network) with one hidden, one input and one output layer was used. The input layer has seven neurons: variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer uses 6 neurons: degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine, respectively, Database for network teaming and test was constructed using a gas turbine performance simulation program. From application of the learned networks to diagnostics of the PW206C turboshaft engine, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.12
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• pp.562-567
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• 2003

Lightning arresters are deteriorated by repetition of protective operation against overvoltages or impulse currents in environments of its use. If a deteriorated arrester is left in power lines, it can lead to an accident such as a line to ground fault even in a normal system. Therefore, it is necessary to eliminate the deteriorated arrester in advance by checking the soundness of arresters on a regular basis, and to ensure the reliability of power systems by preventing accidents. Various deterioration diagnostic techniques and devices are suggested, and most of which measure leakage current components as an indicator of arrester ageing. However, the techniques based on the magnitude of leakage current measure simply RMS or peak value of leakage current components and do not provide detailed information needed in the diagnosis. In this study, we found that the wave height distributions of the total leakage currents are remarkably changed or a new wave height are produced with the progress of arrester deterioration. To propose a new technique for the diagnosis, we designed a leakage current detection unit and an analysis program which can measure leakage current magnitudes and analyze wave height distributions. From the experimental results, we confirmed that the proposed technique by analyzing the wave height distribution can simply diagnose the mode of defects such as a partial damage and an existence of punctures in arresters as well as deterioration of arresters.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.653-656
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• 2004

This paper outlines the three main fieldbus type standards: Foundation Fieldbus; WorldFIP; and the Profibus, each of which have great advantages over traditional instrumentation networking technology. The paper shows, using their specification, how they improve traditional control and data acquisition methods. By analysing the main robust, how the can be used to distribute data around the control system, provided increased diagnostic information, are easy to implement, fault confinement, and move simple control operations from the main controller to the local environment. This will help to segment industrial plants into zones which can control themselves. The main control will then be responsibility high-level control. and interzone communications.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1636_1637
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• 2009

회전기기는 산업설비에서 가장 큰 비중을 차지하는 장치로써 고장발생 시 전체 시스템의 shut-down에 의한 많은 경제적인 손실을 가져오게 된다. 회전기기의 고장은 노화에 의해서도 발생될 수 있지만 베어링 파손이나 축 불일치, 고정 불량등과 같이 다른 원인에 의해서도 발생될 수 있기 때문에 안정적인 설비의 운영을 위해서는 고장진단을 통한 지속적인 관리가 요구된다. 본 연구에서는 회전기기의 지속적인 모니터링 및 고장분석에 사용될 수 있는 DPCM 기법(압축전송기법)을 이용한 CAN 기반의 회전설비 고장진단 시스템을 제안하며 제안된 시스템의 성능검토를 위해 실제 제작된 test-bed 환경에서의 실험을 진행하였다.